Big Data Resources Research Articles

Applying Optimized Algorithms and Technology for Interconnecting Big Data Resources in Government Institutions

The quality of the data in core electronic registers has constantly decreased as a result of numerous errors that were made and inconsistencies in the data in these databases due to the growing number of databases created with the intention of providing electronic services for public administration and the lack of the data harmonization or interoperability between these databases.Evaluating and improving the quality of data by matching and linking records from multiple data sources becomes exceedingly difficult due to the incredibly large volume of data in these numerous data sources with different data architectures and no unique field to create interconnection among them.Different algorithms are developed to treat these issues and our focus will be on algorithms that handle large amounts of data, such as Levenshtein distance (LV) algorithm and Damerau-Levenshtein distance (DL) algorithm.In order to analyze and evaluate the effectiveness and quality of data using the mentioned algorithms and making improvements to these algorithms, through this paper we will conduct experiments on large data sets with more than 1 million records.

Symptom management in adult brain tumours: A literature review.

To review the literature related to symptom management, clinical significance and related theoretical framework systems in adult patients with brain tumours. As understanding of symptoms or symptom clusters and underlying biologic mechanisms has grown, it is apparent that symptom science is moving forward. Although some progress has been made in the symptom science of solid tumours such as breast and lung neoplasms, insufficient attention has been paid to symptom management for patients suffering from brain tumours. Further research is needed to achieve effective symptom management for these patients. A literature review with a systematic search of symptom management in adult brain tumours. Electronic data bases were searched to obtain relevant published literature on symptom management in adults with brain tumours. This was then analysed and a synthesis of relevant findings is presented. Four significant general themes relating to symptom management of brain tumours in adults were identified: (1) The potential theoretical foundation related to symptom management was revealed. (2) Widely accepted validated scales or questionnaires for the assessment of single symptoms or symptom clusters were recommended. (3) Several symptom clusters and the underlying biologic mechanisms have been reported. (4) Specific symptom interventions for adults with brain tumours were collected and classified as evidence-based or insufficient evidence. There are still many challenges in the effective management of symptoms in adults with brain tumours. The guiding role of theoretical frameworks or models related to symptom management should be utilized in future research. Using the concept of symptom clustering for research into symptoms found in patients with brain tumours, exploring common biological mechanisms for specific symptom clusters and making full use of modern big data resources to build a strong evidence base for an effective intervention or management program may inform the management of symptoms among these patients leading to better results. This is a literature review. The ultimate goal is obviously not only improving the survival rate of patients with brain tumours, but also enhancing their quality of life. Several important findings from our review include the theoretical foundations, validated assessment tools, the assessment of symptom clusters and the underlying biologic mechanism, and the identification of the evidence base for symptom interventions. These are of relevance for managers, researchers and practitioners and may function as a reference to help the effective symptom management for adults with brain tumours.

Research on Spatial Form of Cultural Tourism Based on Big Data Resources Construction

Cultural space represents the spatial dimension of the human world, which is symmetrical with time, that is, cultural space must be extended and developed vertically through time. The spatial form of cultural tourism belongs to the concept of multi-scale. Relevant studies analyze the philosophical significance of the spatial form of cultural tourism and define it as the exclusive attribute of intangible cultural heritage. Use big data to actively interact with cultural tourism brands, optimize brand design, promote brand awareness and promote brand image communication. Improve the quality of tourism products through the cross-language big data platform of cultural tourism, and improve the awareness and recognition of tourism enterprises "going out"; As the most important spatial form, cultural field is the real scene of production, life and service in tourist destination, which embodies the social and cultural relationship between people and tourist landscape.

Research on Big Data Resource Management and Optimization Based on Cloud Computing Technology

This paper points out the problems of irrational, complex and low utilization rate of massive data storage in most industries at present, analyzes the meaning and characteristics of big data, thus determining the necessity of distribution and management of big data, providing reference ideas for the management of Chinese big data resources, and further promoting the big data resources to play its due role. To provide support for the transformation of science and technology management decision-making, to achieve social and technological progress. On this basis, from different aspects of the mass data management methods and strategies and the significance and efficiency of these methods to achieve, improve the utilization rate of cluster resources, increase the ability of data sharing, a variety of computing frameworks can also share a distributed storage data, finally through the experiment demonstrated the effectiveness and security of the above strategies.

Machine and deep learning-based stock price prediction during the COVID-19 pandemic: the case of CAC 40 index

PurposeThe goal of this study is to investigate the predictive performance of the machine and deep learning methods in predicting the CAC 40 index and its 40 constituent prices of the French stock market during the COVID-19 pandemic. The study objective in forecasting the CAC 40 index is to analyze if the index and the individual prices will preserve the continuous increase they acquired at the beginning of the administration of vaccination and containment measures or if the negative effect of the pandemic will be reflected in the future.Design/methodology/approachThe authors apply two machine and deep learning methods (KNN and LSTM) and compare their performances to ARIMA time series model. Two scenarios have been considered: optimistic (high values) and pessimistic (low values) and four periods are examined: the period before COVID-19 pandemic, the period during the COVID-19, and the period of vaccination and containment. The last period is divided into two sub-periods: the test period and the prediction period.FindingsThe authors found that the KNN method performed better than LSTM and ARIMA in forecasting the CAC 40 index for both scenarios. The authors also identified that the positive effect of vaccination and containment outweighs the negative effect of the pandemic, and the recovery pattern is not even among major companies in the stock market.Practical implicationsThe study empirical results have valuable practical implications for companies in the stock market to respond to unexpected events such as COVID-19, improve operational efficiency and enhance long-term competitiveness. Companies in the transportation sector should consider additional investment in R&D on communication and information technology, accelerate their digital capabilities, at least in some parts of their businesses, develop plans for lights out factories and supply chains to keep pace with changing times, and even include big data resources. Additionally, they should also use a mix of financing sources and securities in order to diversify their capital structure, and not rely only on equity financing as their share prices are volatile and below the pre-pandemic level. Considering portfolio allocation, the transportation sector was severely affected by the pandemic. This displays that transportation equities fail to be a candidate as a good diversifier during the health crisis. However, the diversification would be worth it while including assets related to the banking and industrial sectors. On another strand, the instability of this period induced an informational asymmetry among investors. This pessimistic mood affected the assets' value and created a state of disequilibrium opening up more opportunities to benefit from potential arbitrage profits.Originality/valueThe impact of COVID-19 on stock markets is significant and affects investor behavior, who suffered amplified losses in a very short period of time. In this regard, correct and well-informed decision-making by investors and other market participants requires careful analysis and accurate prediction of the stock markets during the pandemic. However, few studies have been conducted in this area, and those studies have either concentrated on some specific stock markets or did not apply the powerful machine learning and deep learning techniques such as LSTM and KNN. To the best of our knowledge, no research has been conducted that used these techniques to assess and forecast the CAC 40 French stock market during the pandemic. This study tries to close this gap in the literature.

Pressões institucionais na configuração da capacidade de análise de big data

Abstract This article aims to analyze the setting up of tangible resources and human big data skills, in the face of institutional pressures, in the big data analytics capability in Brazilian companies. Innovation influences the environment in which companies are inserted, increasing uncertainties, resulting in behavioral changes of social players. In response to individual efforts to rationally deal with uncertainties and constraints, organizational homogenization emerges. However, the institutional pressures that influence the setting up of specific resources are still not fully understood in the literature. The replication of the study by Dubey (2019b) is considered, seeing big data technology as an innovation that has caused changes in the social context, thus we seek to grasp the setting up of organizational big data resources in Brazilian companies to build BDA capability, due to institutional pressures. The study makes it possible to see how institutional pressures set up BDA capability, thus being able to provide means to investment allocation decisions in data technology or improve technical management skills in the business intelligence team. The study brought to light the environmental response, resulting from the technological innovation of big data, in Brazilian companies. This demonstrates that organizations adhering to big data technology select their resources in the face of various pressures, in order to build big data analytics capability. This research has a descriptive and quantitative nature, and its operationalization took place through a survey. The research population consists of Brazilian companies that use technology with a large volume of structured and/or unstructured data, to generate results and insights, which support decision making. The survey participants were employees of Brazilian companies that have positions related to building big data analytics capability, located through the LinkedIn platform. 136 valid responses were obtained. To test the hypotheses, the Structural Equation Modeling technique was used by means of the software Smartspls v. 3.2.3. This study contributes by bringing an understanding of organizational behavior in the face of institutional pressures (coercive, normative, and mimetic) when selecting tangible resources and human big data skills to build BDA capability, using Resource-Based Theory. It is observed that the setting up of BDA capability is influenced by tangible resources and human skills. Tangible resources are selected due to formal pressures, competitive conditions, and by imitating existing standards in the market. Meanwhile, the required human skills are impacted, through legitimation and professional networks of decision makers.

MESOECONOMIC MARKET ANALYSIS BASED ON SELECTED BIG DATA TOOLS – THE CASE OF WOOD MARKET TRENDS IN POLAND UNDER ECONOMIC UNCERTAINTY

The paper discusses selected trend changes in the Polish wood-based products market under conditions of economic uncertainty. From a mesoeconomic approach, using Big Data resources and online e-commerce tools, the wood market was analyzed from the perspective of demand and online user activity. In terms of time scope, the research covered the period 2019-2021, including the pandemic year. Secondary data based on Google Ads and Google Analytics were analyzed. The study used selected open-source analytical tools, including Google Keyword Planner and Data Studio. The controversial hypothesis, which assumes that potential demand for wood and wood-based products shows a sustainable trend even under economic uncertainty, was verified. The findings were visualized and discussed. As a result, although the rationale for confirming the assumed hypothesis was not obtained. However, the results of the research proved to be interesting, out of the box. It turned out that the popularity and multifaceted use of wood is not limited by typical barriers. The consequence of the market situation is a growing demand for wood, but at the same time a supply deficit. This is a challenge for the wood market benchmarking and formulating practical recommendations for economic policy in the wood-based sector in Poland.

Edge computing for vehicle battery management: Cloud-based online state estimation

The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.

Impact of big data resources on clinicians’ activation of prior medical knowledge

BackgroundActivating prior medical knowledge in diagnosis and treatment is an important basis for clinicians to improve their care ability. However, it has not been systematically explained whether and how various big data resources affect the activation of prior knowledge in the big data environment faced by clinicians. ObjectiveThe aim of this study is to contribute to a better understanding on how the activation of prior knowledge of clinicians is affected by a wide range of shared and private big data resources, to reveal the impact of big data resources on clinical competence and professional development of clinicians. MethodThrough the comprehensive analysis of extant research results, big data resources are classified as big data itself, big data technology and big data services at the public and institutional levels. A survey was conducted on clinicians and IT personnel in Chinese hospitals. A total of 616 surveys are completed, involving 308 medical institutions. Each medical institution includes a clinician and an IT personnel. SmartPLS version 2.0 software package was used to test the direct impact of big data resources on the activation of prior knowledge. We further analyze their indirect impact of those big data resources without direct impact. Results(1) Big data quality environment at the institutional level and the big data sharing environment at the public level directly affect activation of prior medical knowledge; (2) Big data service environment at the institutional level directly affects activation of prior medical knowledge; (3) Big data deployment environment at the institutional level and big data service environment at the public level have no direct impact on activation of prior knowledge of clinicians, but they have an indirect impact through big data quality environment and service environment at the institutional level and the big data sharing environment at the public level. ConclusionsBig data technology, big data itself and big data service at the public level and institutional level interact and influence each other to activate prior medical knowledge. This study highlights the implications of big data resources on improvement of clinicians’ diagnosis and treatment ability.

Research on the Application of Information-Based Big Data Fusion Technology in College English Education Design

English is the most widely used language in the world, and China’s economic development has become the second largest country in the world. Therefore, popularizing English in China is the need of national conditions and the desire for talents who are good at English majors. In the current traditional college English education mode and in the external environment, college students’ English knowledge has not been substantially expanded, which has seriously caused the social needs not being met and students not being able to show their self-worth. Based on the deficiency of traditional college English teaching mode, this paper designs an efficient and perfect college English education mode by combining information big data fusion technology, so as to ensure that students can really love English and learn English more easily. The results of the experiment are as follows: (1) college English education and students’ English learning are analyzed, and the research direction of the experiment is approved, and the functional has been investigated, which lays a foundation for the experimental research of this paper; (2) old-fashioned college English education mode: intelligent big data technology is applied to English education. By using the information big data fusion algorithm to ensure the qualified English teaching effect, it not only helps teachers effectively combine big data resources but also changes the atmosphere of teaching classes, constructs modern language education mode, and promotes students’ thinking mode of exploring language learning.

Big data-driven intelligent governance of college students' physical health: System and strategy.

With the development of information technology, the application of a new generation of information technologies, such as big data, Internet Plus, and artificial intelligence, in the sports field is an emerging, novel trend. This paper examined the relevant research results and literature on physical education, computer science, pedagogy, management, and other disciplines, then used a self-made questionnaire to investigate the physical health status of Chinese college students. The big data were subsequently analyzed, which provided a scientific basis for the construction of an intelligent governance system for college students' physical health. Intelligent devices may be used to obtain big data resources, master the physical sports development and psychological status of college students, and push personalized sports prescriptions to solve the problems existing in college students' physical health. Research shows that there are four reasons for the continuous decline in Chinese college students' physical health levels. These are students' lack of positive exercise consciousness and healthy sports values (85.43%), a weak family sports concept and lack of physical exercise habits (62.76%), poor implementation of school sports policies (55.35%), and people's distorted sports value orientation (42.27%). Through the connecting effect of data, we can bring together the positive role of the government, school, society, family, and students so as to create an interlinked impact to promote students' physical health. The problems of insufficient platform utilization, lack of teaching resources, lagging research, and insufficient combination with big data in the intelligent governance of physical health of Chinese college students can be solved by building an intelligent governance system of physical health. Such a system would be composed of school infrastructure, data resources and technology processing, and intelligent service applications. Among these, school infrastructure refers to the material foundation and technical support. The material foundation includes perceptions, storage, computing, networks, and other equipment, and the technical support includes cloud computing, mobile Internet, the Internet of Things, artificial intelligence, and deep learning. Data resources refer to smart data, such as stadium data, physical health management data, and students' sports behavior data, which are mined from data resources such as students' physical development, physical health, and sports through big data technology and intelligent wearable devices. Intelligent managers provide efficient, intelligent, accurate, and personalized intelligent sports services for college students through data resource value mining, venue space-time optimization, health knowledge discovery, sports prescription pushes, etc. Finally, we put forward the development strategy for further deepening and improving the big data-driven intelligent governance system for college students' physical health. The intelligent governance system of physical health driven by big data and its development strategy can not only accurately guide and improve the physical health level of college students but also realize integrated teaching inside and outside physical education classes.

Regulatory Mechanism of Financial Market Resource Management Driven by Big Data

In order to further understand the current situation of the financial market and better supervise the resource management of the financial market, combined with big data and cloud computing technology, through the construction of big data cloud platform resource management system and the integration of various technical computing frameworks, we can realize the effective supervision of big data resources in the financial market. Using J2EE technology, this paper analyzes, designs, implements, and tests the investment data management system, analyzes the content of the software engineering subject, and obtains the demand function description of the business. According to the software development process and the actual situation of enterprise investment, this paper expounds the basic requirements of the investment data management business, system architecture requirements, user use case status, and the operation and configuration environment of the investment data management system. This paper analyzes the technical characteristics and operation indicators of the software, and establishes the data flow for the data related to investment data management, such as information statistics, data query, information classification and so on. Finally, the system is verified, operated and tested, and the business use cases and parameters of the system are tested according to the two indicators of software testing. The basic functions of the investment data management realized by the system are correct, the design is reasonable, the operation is stable, the operation response time is short, the operation accuracy is high, and the data access efficiency is good.

Multivariate understanding of income and expenditure in United States households with statistical learning

In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in multivariate techniques and learning methods, in practice the analysis and interpretations are often focused on a small portion of available data and limited to a single perspective. This paper aims to utilize a selected array of multivariate statistical learning methods in the analysis of income and expenditure patterns of households in the United States using the Public-Use Microdata from the Bureau of Labor Statistics Consumer Expenditure Survey (CE). The objective is to propose an effective data pipeline that provides visualizations and comprehensive interpretations for applications in governmental regulations and economic research, using thirty-five original survey variables covering the categories of demographics, income and expenditure. Details on feature extraction not only showcase CE as a unique publicly-shared big data resource with high potential for in-depth analysis, but also assist interested researchers with pre-processing. Challenges from missing values and categorical variables are treated in the exploratory analysis, while statistical learning methods are comprehensively employed to address multiple economic perspectives. Principal component analysis suggests that after-tax income, wage/salary income, and the quarterly expenditure in food, housing and overall as the five most important of the selected variables, while cluster analysis identifies and visualizes the implicit structure between variables. Based on this, canonical correlation analysis reveals high correlation between two selected groups of variables, one of income and the other of expenditure.

Artificial intelligence for antibody reading comprehension: AntiBERTa

Utilizing publicly available antibody big data resources, Leem et al. (2022) developed an antibody-specific language model, AntiBERTa, to understand the “language” of antibodies. Case studies reveal that AntiBERTa can be an extremely useful tool for antibody engineering.

Effect Evaluation of Eco-Environmental Big Data Resource Integration and Data Sharing Construction

The application of big data in the field of ecological environment has achieved remarkable results, but there are still problems such as insufficient innovation and lack of shared service ability. This research takes the big data platform as the technical support and makes full use of advanced technologies such as internet of things, cloud computing, and big data to realize the integration of ecological environment big data resources and real-time data sharing. Through network technology, the ecological environment metadata can be automatically obtained from the internet and stored. When sharing ecological environment metadata, realize the data response and declassification of geospatial data for different user access levels, and finally safely feedback the ecological environment spatial big data integrated with the target ecological environment metadata and declassified geospatial data to the access initiator, so as to realize the centralized sharing of decentralized environmental information resources and give full play to the supporting role of big data, so that big data can be effectively applied in environmental monitoring. Find out the changes of pollution sources and pollutants through massive data, so as to provide necessary data support for the whole environmental monitoring.

Multimedia Automation Access Control of Big Data Open Resources Based on Blockchain.

In order to better mine the value of data, the author proposes a research on the automatic access control of big data open resources multimedia based on blockchain and introduces big data access control BBAC-BD (blockchain-based access control mechanism for big data environment). The author designed a strategy management contract based on the Bloom filter, as a probabilistic data structure with extremely high space utilization efficiency and proposed the strategic management contract (PAP CONTRACT) and the strategic decision contract (PDP CONTRACT). In this way, the nontampering, auditability, and verifiability of the access control information are guaranteed; then, the access control method based on smart contracts is adopted to realize the user-driven, whole-process transparent, and dynamic and automatic access control of big data resources. The simulation results show that the greater the ratio of n/k, the better the optimization effect, and the greater the ratio, the lower the corresponding misjudgment rate, but it will also take up more space costs. At the same time, the true value of the false positive rate is generally less than the theoretical value of the false positive rate. When the performance of Hash (strategy to retrieve) is better, the result of Hash distribution is more uniform. Under the condition of m = 3, the misjudgment rate acceptable for the expected use can be achieved, and the increase in the number of Hashes will not bring a significant increase in revenue. Freed from the traditional model of providing access control services based on third parties, solve the problem of transparency of authority judgments; at the same time, through smart contracts, based on the strategy published by the resource owner on the blockchain, realize automatic access control to big data resources; and make the judicial process more flexible and the judgment result more credible. The BBAC-BD mechanism realizes a safe, reliable, and transparent new access control architecture, and it can effectively promote the safe circulation and sharing of big data.

Research on Blockchain Technology and Media Industry Applications in the Context of Big Data

With the everyday use of big data technology, using big data resources effectively has become a key area of research. Media consumption creates enormous amounts of data in people’s digital lives. This data is not only very valuable to the company, but it also presents a number of concerns, including privacy breaches and data exploitation. Along with the continuous improvement of blockchain technology, the rational use of big data resources has become possible. Blockchain technology has expanded from the initial Bitcoin to the financial field and has become more widely used in the legal and media industries. This study catches the actual cases of blockchain technology in the media industry through Qingbo Big Data, analyzes the current global blockchain and media industry’s overall development status and the existing problems, and puts forward corresponding countermeasures to accelerate the integration and development of blockchain and media industry in the context of big data. The results show that the technical features of blockchain can solve the painful problems of copyright risk, algorithmic black box, and weak trust mechanism in the media industry, and the combination of the two can form a new burst of media productivity. The article discusses the broad application prospects of blockchain in the media industry, which is essential for big data and blockchain research. This paper composes the pioneering literature on the blockchain, catches the information on blockchain by keywords, analyzes the application prospect of blockchain in the media industry, and promotes the research and application of blockchain technology.

Mechanism of Impact of Big Data Resources on Medical Collaborative Networks From the Perspective of Transaction Efficiency of Medical Services: Survey Study.

BackgroundThe application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained.ObjectiveThis study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs.MethodsThis study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact.ResultsSharing of diagnosis and treatment data (β=.222; P=.03) and sharing of medical research data (β=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (β=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions.ConclusionsThis study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs.

Research on Basic Information of Enterprise Electronization under the Background of Big Data

Ensuring information security is a maintenance process that combines dynamic and static. First of all, you can use a series of protective tools, such as firewalls, operating system authentication, encryption, and so on. At the same time, use some dynamic detection tools. With the rapid development of Internet technology, big data, cloud computing, and other technical terms are no longer strange to people. Today's society is increasingly networked, creating a common platform for big data resource sharing and data communication in various industries. Big data and cloud computing technology have been applied to every field of human life with a strong penetration, resulting in technological innovation in various industries. Under the background of the rapid development of big data and cloud computing technology, the traditional enterprise form has been difficult to cope with the increasingly fierce competitive environment, making the electronic enterprise become the inevitable trend in the development and reform of enterprises in today's world. Electronic enterprises promote the efficient and vigorous development of enterprises, improve the speed, efficiency, and market competitiveness of enterprises, and better adapt to the changes in the market environment caused by big data and cloud computing. With big data and cloud computing as the background, this paper studies and analyzes the inevitability, basic characteristics, existing problems, countermeasures, and other basic information of enterprise electronization, so as to provide theoretical guidance for enterprise electronization. It has the ability to control the dissemination and content of information, ensure the censorship of information, and provide the basis and means of investigation for emerging network security issues.

Construction of a Smart Library Subject Precise Service Platform Based on User Needs

Under the development and support of digital, networked and intelligent technologies (IT), university wisdom libraries adopt superb IT, sophisticated big data resource allocation, fine supervised wisdom RFD management system and exquisite supervised wisdom service system to provide a wisdom environment to meet users’ personalized needs and promote the transformation of libraries to a new form of wisdom. University intelligent libraries are still in their infancy and development stages in today’s world; the infrastructure has not been fully developed, the number of intelligent devices, the types of intelligent services, and the level of intelligence have all been developed but are still in their infancy and development stages, and the level of intelligence has not yet reached a significant level, and the level of intelligence has not yet reached a significant level. This paper develops the construction of a precise service platform for wisdom library disciplines based on users’ needs. In this paper, we propose a hybrid continuous ant colony optimization (BACO) method, and further propose a BACO-based multidoor image segmentation bun type for facial image segmentation. The experiments show that the proposed method has good performance and can lay the foundation for the next step of facial recognition and even the construction of smart library to ensure the accurate service of university smart library.