Quality Evaluation Model Research Articles

Two-level integrated verification evaluation method based on comprehensive weighted assessment in multiple scenarios

The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

Quality Evaluation and Optimization of Idle Goods Swap Platform Based on Grounded Theory and Importance–Performance Analysis

Given economic downturns and environmental concerns, promoting innovative consumption patterns is urgently needed. Swap is a promising collaborative consumption pattern that redistributes ownership of goods without using money. In the past, no research has focused on swap platform quality due to a lack of empirical resources. Therefore, this study aims to construct theoretical methods for evaluating and optimizing the quality of swap platforms. Analyzing consumer characteristics and motivations, the study creates a user profile that guides swap platforms’ market positioning. Then a quality evaluation model for swap platforms is constructed to identify areas for improvement. Data were collected from core users of a start-up swap platform in Shanghai, including 20 in-depth interviews and 180 survey questionnaires. The findings reveal that the main consumers in Shanghai’s swap market are highly educated women aged 20 to 40, with upper-middle incomes. They are motivated by functional, charitable, economic, and hedonic factors. Using grounded theory and the IPA method, this study developed a quality evaluation model for swap platforms, consisting of 18 indicators that clearly distinguish areas of weakness and strengths. This study provides valuable insights into the methodological research of quality evaluation for swap platforms and offers practical suggestions for their optimization.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Knowledge Atlas of Cultivated Land Quality Evaluation Based on Web of Science Since the 21st Century (2000–2023)

Cultivated land is the most important natural resource for human survival and development. The quality of cultivated land is closely related to grain output, and whether it can guarantee stable food supply is directly related to national food security. Cultivated land quality evaluation is an effective tool for understanding and mastering cultivated land quality. However, few studies have applied bibliometrics to quantitatively and systematically analyze this field. We used VOSviewer 1.6.19 and CiteSpace 6.3.1 software to visually analyze and construct 2478 documents related to cultivated land quality evaluation retrieved from the Web of Science core collection database from 2000 to 2023. Results show that cultivated land quality evaluation is still a popular research field. The collaboration ability among authors is weak and the distribution of institutions and countries publishing in this field is very uneven. In addition, the relevant research has been published in a variety of journals such as agriculture, environment, ecology, and computer technology. The research content is becoming more and more interdisciplinary. Keywords such as “Soil quality”, “Swat”, “Remote sensing”, “Heavy metals” and “Ecosystem services” have become hot topics in this field. In the future, it is necessary to further deepen the connotation of cultivated land quality, develop a long time series dynamic model of cultivated land quality evaluation and monitoring, and enhance the transformation of research results into practical applications.

Ensemble machine learning models for sperm quality evaluation concerning success rate of clinical pregnancy in assisted reproductive techniques

This study aimed to investigate the influence of various sperm quality characteristics, including morphology, motility, and count, on the success rates of clinical pregnancy achieved through assisted reproductive technologies (ART) such as in-vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and intrauterine insemination (IUI). The secondary objective was to assess the impact of these sperm parameters on the clinical pregnancy rate that resulted in the detection of a fetal heartbeat during the 11th week of gestation, a crucial milestone in successful ART-derived pregnancies. The researchers employed a retrospective analysis, evaluating data from 734 couples undergoing IVF/ICSI and 1197 couples undergoing IUI across two infertility centers. Exclusion criteria included cases involving donated eggs or sperm, surrogate uteri, and infertile couples with combined male and female factors. Five ensemble machine-learning models were utilized to predict the clinical pregnancy success rates. The Random Forest (RF) model achieved the highest mean accuracy (0.72) and area under the curve (AUC) (0.80), outperforming the other models for both IVF/ICSI and IUI procedures. The Shapley Additive Explanations (SHAP) value analysis revealed that for IUI cycles, all three sperm parameters (morphology, motility, and count) had significant negative impacts on the prediction of clinical pregnancy success. In contrast, for IVF/ICSI cycles, sperm motility had a positive effect, while sperm morphology and count were negative factors. In cycles with 1 to 5 retrieved eggs, sperm motility, and count, they positively affected the clinical pregnancy rate. The study also identified cut-off values for sperm count, with 54 and 35 being the respective thresholds for IVF/ICSI and IUI. Additionally, a significant cut-off point 30 was found for the sperm morphology parameter across all procedures. This study underscores the immense potential of leveraging ensemble machine learning models with traditional sperm quality assessments. This integrated approach can elevate the precision and personalization of clinical decision-making in the field of assisted reproductive technologies, ultimately offering more hope and better outcomes for couples struggling with infertility.

Research on the association mechanism and evaluation model between EMG data and product aesthetic quality in product aesthetics evaluation

Product aesthetics play a crucial role in user satisfaction and product success. However, the feasibility and accuracy of using electromyographic (EMG) data for product aesthetic quality evaluation are unknown. Here, we analysed the correlations and associations between EMG physiological data and product aesthetic quality of 60 subjects by SPSS software and designed a product aesthetic quality evaluation model based on the multivariate logistic (M) algorithm to answer this question. It was found that the EMG data of the zygomatic and buccal muscles did not have significant correlations with product aesthetic quality, whereas five indicators in the EMG data of the frowning muscles had significant correlations relationships with product aesthetic quality. We also found that the overall accuracy of the product aesthetic quality evaluation model was 72.3%, which was able to better distinguish product aesthetic quality. This study demonstrates that product aesthetic quality can be evaluated using EMG data and that the product aesthetic quality evaluation model can provide an objective and accurate decision-making reference to help engineers and designers in product aesthetic quality evaluation and product design. This study lays the foundation for the future integrated multimodal physiology to conduct aesthetics quality evaluations with a high degree of accuracy.

New method of evaluating spatial quality for innovation districts: A case study of Haidian and Chaoyang districts, Beijing

Science and technology innovation has become the core driving force of urban development, and innovation spaces as the carriers of innovation activities have evolved continuously, forming the emerging model of innovation districts in recent years. However, the evaluation standard of innovation spaces has not changed with the evolution of the model, and the evaluation method that focuses on economic benefits still dominates the mainstream. As a result, the construction of innovation spaces around these indicators and guidelines lags behind the advanced level.In this paper, we have systematically sorted out the relevant research progress of innovation districts, established a set of cases with global typicality, combined the dual attributes of “innovation characteristics + urban characteristics,” constructed a research framework for the spatial quality of innovation districts based on the dual perspectives of “innovation space,” and summarized the spatial quality evaluation model through analysis. We do an empirical study on Haidian District to find out how well innovation districts work in terms of space, find typical clusters, and come up with the most important aspects of their space quality. In order to observe the applicability of this method, it is also applied to the urban health check-up in Chaoyang District, Beijing. Through the expert seminar, the evaluation and analysis method of spatial quality of innovative urban areas is further verified and discussed. The results show that the integration of innovative urban areas and urban structures, and the collaborative development of innovative industries and high-quality spaces are the keys to promote the common development of innovative economy and cities. The new evaluation method can effectively measure the quality of innovation spaces, and has certain transferability.

Exploring critical quality indicators and developing a non-destructive detection method using near-infrared spectroscopy for sea bass (Lateolabrax japonicus) quality evaluation

In this study, chemometrics were employed to explore the relationship between sensory evaluation and physicochemical indicators of sea bass (Lateolabrax japonicus). Through principal component analysis, cluster analysis, and Pearson correlation analysis, three pivotal indicators were identified: protein content, b* value, and condition factor. Leveraging the grey relational analysis, weights were assigned to these three core quality indicators, resulting in a comprehensive sea bass quality evaluation model: Y = 0.911 × protein (g/100 g) + 0.742 × b* + 0.747 × condition factor. Moreover, near-infrared spectroscopy combined with chemometrics were employed to evaluate the quality of sea bass. The different origins of sea bass were accurately distinguished using orthogonal partial least squares discriminant analysis. The partial least squares regression model was constructed for predicting the critical quality indicator, protein content, with R2P of 0.926. This study offers new insights for developing rapid, economical, and reliable methods for assessing aquatic product quality.

Modelling and nondestructive quality prediction of Guanxi Honey Red Pomelo (<i>Citrus grandis</i> (L.) Osbeck) based on shape characteristics and bitterness

Currently, it is difficult to distinguish the inner quality of pachycarpous fruits using nondestructive instruments, and trace nutrients and flavor compounds are rarely considered in fruit sorting. In this study, the external features and internal quality indices of an extensively farmed citrus fruit, the Guanxi honey red pomelo, were measured. The correlation between the inner and external quality was analyzed using Pearson correlation, canonical correlation, and grey correlation analyses. This research innovatively introduced bitter substances and revealed that the height-diameter ratio could serve as a predictor for ultra-size fruits, which partially reflects inner quality indices. Several grey formulas were developed to express these correlations and were fitted to a matrix, which demonstrated accuracies at all level 1 (C < 0.35, P > 0.95). Based on the matrix, fruit quality analysis of white and red pomelo showed high prediction accuracy in TSS and bitter substance indices (R2 = 0.6 – 0.8) and indicated that mature and symmetrical pomelo (height-diameter ratio of 0.9 – 1.1) exhibit a similar quality pattern. This study achieved rapid quality assessment using easy measurable shape characteristics and explored the application of characteristic bitter substances in the quality evaluation model, providing theoretical references and potential application guidance for pachycarpous fruit sorting, as well as introducing a new idea to incorporating characteristic quality into modeling index.

Establishment of near-infrared rapid prediction model and comprehensive evaluation model for foxtail millet quality

Foxtail millet (Setaria italica) is one of the earliest cereal crops in the world and is still an important food crop in Asia, Europe and Africa. However, there are few reports on rapid and comprehensive evaluations of foxtail millet quality. In this study, color, nutrient content, cooking quality and sensory quality of 45 varieties of foxtail millet from the main production areas of the country were determined. Near infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) was developed for rapid and nondestructive detection of foxtail millet quality, where NIRS models for ash, amylopectin, protein, and VB1 and VB2 were desirable. In addition, correlation analysis, principal component analysis and cluster analysis were used for comprehensive evaluation of millet quality. The results showed that foxtail millet with low ash, protein, fat and fiber content and high amylopectin and carotenoid content has good cooking quality and was more preferred. This research will contribute to the quality evaluation and further processing of foxtail millet.

Comprehensive physicochemical indicators analysis and quality evaluation model construction for the post-harvest ripening rapeseeds

Rapeseed (Brassica napus L.) is the second largest globally cultivated oil crop, but the effects of post-harvested ripening on rapeseed quality is unclear and unpredictable. This study reveals the relationship between post-harvest ripening periods (PHR) and physicochemical quality of different rapeseed cultivars using comprehensive physicochemical indicators analysis. The results indicate that PHR led to a gradual decrease in chlorophyll, carotenoid and moisture content but continually increased oil and total phenol content (TPC). Besides, 295 lipid molecules from 13 lipid subclasses were identified, revealing that the relative content of triacylglycerol (TG) was progressively increased while diacylglycerol (DG) demonstrated a consistent decline throughout the PHR. Correlation analysis, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to construct and verify the comprehensive quality evaluation model for rapeseeds in PHR. This paper develops a comprehensive quality evaluation model for post-harvest ripening rapeseeds and advances the development of agricultural products.

Research on the Construction and Application of Service Quality Evaluation Models for Scenic Spots: Taking Chongqing Ciqikou as an Example

In the current environment of increasingly fierce competition in the tourism industry, service quality has become crucial for enhancing the competitiveness of scenic spots. This paper uses the SERVQUAL model to design a service quality evaluation questionnaire that captures the gap between tourists’ expectations and perceptions, using the Ciqikou Scenic Spot as a case study. Data collected from field surveys are used to comprehensively and meticulously evaluate the service quality of the Ciqikou Scenic Spot. The analysis results show that the scenic spot, with its unique folk culture experience and beautiful ecological environment, has certain advantages in terms of service quality. However, significant deficiencies exist in infrastructure and environmental hygiene. Accordingly, targeted improvement suggestions are proposed to further enhance the service quality of the Ciqikou Scenic Spot and meet the increasingly diverse and personalized needs of tourists. This study provides not only a specific service quality improvement strategy for the Ciqikou Scenic Spot but also a valuable reference for other tourist attractions.

Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street

The exploitation of urban subsurface space in urban inventory planning is closely connected to the quality of urban environments. Currently, the construction of underground pedestrian streets is characterised by inefficiency and traffic congestion, making them insufficient for fulfilling the demand for well-designed and human-centred spaces. In the study of spatial quality, traditional evaluation methods, such as satellite remote sensing and street maps, often suffer from low accuracy and slow updating rates, and they frequently overlook human perceptual evaluations. Consequently, there is a pressing need to develop a set of spatial quality evaluation methods incorporating pedestrian perspectives, thereby addressing the neglect of subjective human experiences in spatial quality research. This study first quantifies and clusters the characteristics of underground pedestrian spaces using spatial syntax. It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. The development of this model offers valuable scientific guidance for the planning and construction of high-quality urban public spaces.

Coordinated analysis of groundwater spatiotemporal chemical characteristics, water quality, and potential human health risks with sustainable development in semi-arid regions.

The emergence of large-scale time-series data and advancements in computational power have opened new avenues for analyzing the spatiotemporal evolution of groundwater chemistry, water quality, and human health risks. This paper utilizes hydrogeochemical methods to elucidate the controlling factors of water chemical components based on the test results of 124 groundwater samples collected from 31 monitoring wells in Fuxin City, Liaoning Province, China, from 2018 to 2021. By integrating the Random Forest and Enhanced Water Quality Index methods for water quality assessment and employing the Human Health Risk Assessment (HHRA) model to analyze human health risks, our findings indicate that the groundwater is mildly alkaline, with SO4·Cl-Ca·Mg and HCO3-Ca·Mg as the dominant hydrochemical types, primarily derived from the dissolution of carbonate and silicate minerals such as dolomite, limestone, and andesite, and cation exchange reactions. The EI_RF water quality evaluation model reveals that the overall water quality in the study area is poor, with Class I and II water quality zones mainly located in the northeastern and central parts of the study area, showing a gradual transition from Class I and II in the northeast to Classes IV and V in the southwest, significantly influenced by NO3-, TH, TDS, and SO42-. The HHRA model results indicate that the potential non-carcinogenic risk of groundwater nitrates has a severe impact on infants, with the spatial distribution being low in the northeast and high in the southwest. Due to industrial activities, agricultural practices, and population growth, certain areas in developing countries such as China and India exhibit nitrate concentrations significantly higher than those in most international regions, highlighting global environmental and public health challenges. This underscores the importance of enhancing groundwater monitoring and implementing measures to mitigate pollution. These research outcomes hold significant implications for the government in formulating rational protection and management measures to ensure the sustainable utilization of groundwater resources.

Optimization Strategies for Waterfront Plant Landscapes in Traditional Villages: A Scenic Beauty Estimation–Entropy Weighting Method Analysis

This investigation delves into the waterfront plant landscapes of traditional villages in Western Hunan, China, aiming to bolster sustainable ecological resource management, amplify ecological culture, and ameliorate environmental standards. Furthermore, it endeavors to furnish a theoretical scaffold for the meticulous construction and assessment of these landscapes. This study has illustrated the waterfront botanical landscapes of 32 traditional hamlets within the Xiangxi region, integrating prior research on the waterfront botanical regression model based on the Scenic Beauty Estimation (SBE) method. It established and investigated fifteen landscape factors pivotal to the aesthetic valorization of these village waterfronts. The study concocted a beauty quality evaluation model, unearthing a significant correlation (p < 0.01) across evaluations by students majoring in landscape architecture, expert landscape architects, and laypersons, thus underscoring a consensus in aesthetic judgments. A noteworthy correlation between the beauty value (Z-value) and the entropy weight value was elucidated through the equation EWM = −0.106 + 0.425ZSBE, showcasing the landscape quality’s variance among the studied villages. The formulated evaluation model accentuates the significance of seasonal variation, scale affinity, and a rich hierarchical structure.

Fuzzy Evaluation Model for Critical Components of Machine Tools

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools.

Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

Perceived Quality of Basic Sanitation: an Analysis From the Four Components

Objective: The study aims to identify users' perceptions regarding the quality of public basic sanitation services based on its four components. Theoretical Framework: Based on existing legislation and the quality of public services, this study focuses on the four components of basic sanitation services and their problems, aiming to investigate users' perceptions. Method: Survey research encompassing 1,114 residents in the state of Rio Grande do Sul. The instrument covered the four components of sanitation (potable water supply, sanitary sewage, urban cleaning and solid waste management, and urban stormwater drainage and management). Descriptive statistics and multiple linear regression analysis were used as analysis techniques. Results and Discussion: The quality of basic sanitation services in all its components is considered satisfactory by users, but improvements are still needed. The frequency of observed problems negatively impacts the perception of service quality. Those who reuse water, separate waste, and whose residences have sewage collection and treatment and stormwater drainage systems attribute higher quality to the service. Research Implications: There is a need to implement a continuous quality evaluation model for sanitation services, as without evaluation, end-user feedback does not exist. Originality/Value: Identification of users' perceptions regarding the quality of basic sanitation services, as no research with this theme covering the entire state has been found so far.

The analysis of teaching quality evaluation for the college sports dance by convolutional neural network model and deep learning

This study aims to comprehensively analyze and evaluate the quality of college physical dance education using Convolutional Neural Network (CNN) models and deep learning methods. The study introduces a teaching quality evaluation (TQE) model based on one-dimensional CNN, addressing issues such as subjectivity and inconsistent evaluation criteria in traditional assessment methods. By constructing a comprehensive TQE system comprising 24 evaluation indicators, this study innovatively applies deep learning technology to quantitatively assess the quality of physical dance education. This TQE model processes one-dimensional evaluation data by extracting local features through convolutional layers, reducing dimensions via pooling layers, and feeding feature vectors into a classifier through fully connected layers to achieve an overall assessment of teaching quality. Experimental results demonstrate that after 150 iterations of training and validation on the TQE model, convergence is achieved, with mean squared error (MSE) decreasing to 0.0015 and 0.0216 on the training and validation sets, respectively. Comparatively, the TQE model exhibits significantly lower MSE on the training, validation, and test sets compared to the Back-Propagation Neural Network, accompanied by a higher R2 value, indicating superior accuracy and performance in data fitting. Further analysis on robustness, parameter sensitivity, multi-scenario adaptability, and long-term learning capabilities reveals the TQE model's strong resilience and stability in managing noisy data, varying parameter configurations, diverse teaching contexts, and extended time-series data. In practical applications, the TQE model is implemented in physical dance courses at X College to evaluate teaching quality and guide improvement strategies for instructors, resulting in notable enhancements in teaching quality and student satisfaction. In conclusion, this study offers a comprehensive evaluation of university physical dance education quality through a multidimensional assessment system and the application of the 1D-CNN model. It introduces a novel and effective approach to assessing teaching quality, providing a scientific foundation and practical guidance for future educational advancements.

Research on Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities Under Big Data Environment

In innovation and entrepreneurship education, how to effectively use big data technology to improve the quality of education has become an urgent problem. In this article, the authors summarize the development of big data technology and its application background in higher education and point out the limitations of traditional education quality evaluation methods. They discuss the research status of the quality evaluation of innovation and entrepreneurship education in colleges and universities at home and abroad and the application of big data in education quality evaluation. Using the fuzzy comprehensive evaluation method, they constructed a quality evaluation model of innovation and entrepreneurship education in colleges and universities based on big data and proposed a corresponding evaluation process and index system. Finally, through empirical analysis, they verified the effectiveness and operability of the evaluation model. This research has theoretical and practical significance for improving the quality of innovation and entrepreneurship education in colleges and universities.