Study of Status Quo and Countermeasures of Foreign College Students ' Mental health

At present, the development of higher vocational education has become a hot issue in the whole field of education. At the same time, mental health education for college students in higher vocational colleges has also received extensive attention, especially how to improve the psychological quality of college students through effective mental health education mode. Under this background, this paper analyzes the mental health of college students in higher vocational colleges by using computer big data technology, and explores the construction method of the new mental health education model for higher vocational college students based on computer big data technology.

Nowadays, there is an increase in attention to the college student's mental health, and to enhance the awareness related to college students' mental health, colleges and universities have executed an immense range of mental health publicity activities. In order to better combine deep learning with classroom teaching, this paper puts forward a deep learning algorithm formulated on convolutional neural networks. The purpose of this research is to investigate the development and use of a cultivation mechanism for mental health education of college students in campus culture creation from the perspective of deep learning. The study's primary goal is to comprehend college students' mental health training in campus culture creation. The study's objective is to develop experimental outcomes of college students utilizing mental health education courses as an optional or mandatory course. Finally, investigations related to college students' mental health from the current situation in China, the investigation, statistics and analysis related to the college students in China are carried out in this situation. The experimental results of this study show that 62 of the 156 schools and universities assessed provide courses on mental health education for college students that are both obligatory and optional. According to the students questionnaire survey, 86.7% of respondents believe that it is critical to establish mental health related educational courses, 61.9% believe that compulsory courses should be established, and students want to add group guidance or activities to the teaching process to improve their experience and participation.

College students' mental health education is crucial for holistic individual development and societal quality. It shapes well-rounded personalities, fosters innovation, and cultivates responsible citizens, contributing to social stability and national development. Our research aims to establish an evaluation system for college student mental health education policies based on the Policy Modeling Consistency Index (PMC-Index) model and text mining techniques, quantitatively assessing 15 representative policies. This paper first retrieves policy documents through specialized policy databases and government websites, excluding policies that have already expired or become invalid. The sample selections of this research range from 2001 to 2024, including national guidelines and specific actions. Referring to the above steps of policy effectiveness evaluation, our research comprehensively analyses the implementation effectiveness of the foregoing policies regarding mental health curricula, service systems, crisis intervention, and family cooperation. In the following, based on 10 major variables, 38 sub-variables are constructed with a binary coding system to quantify the content of policy for objectivity. Then, the ROSTCM 6.0 software is used for text segmentation and word frequency statistics, meanwhile, keywords and semantic networks of core policies will be considered for analysis. Subsequently, the PMC-Index is computed based on the multi-input-output matrix and a diagram of the PMC-Surface is drawn with the help of MATLAB to express policy consistency and deficiencies in different dimensions intuitively. Among the 15 policies, 11 exhibit Great Consistency (GC), and 4 fall under Acceptable Consistency (AC). Higher scores are found in Policy Function (X6), Policy Evaluation (X8), and Policy Goals (X9), indicating practical implementation and clear guidance. However, lower scores in Policy Nature (X1), Policy Timeliness (X2), Policy Issuing Agency (X3), and Policy Object (X4) reveal deficiencies in policy innovation, long-term planning, and target group coverage. Particularly, short-term policies lack sustainability, limiting their long-term effectiveness in supporting students' mental health. Through PMC-Surface analysis, it is found that lower scores in timeliness and adaptability to target groups are primary factors contributing to surface depressions in the diagram, suggesting that these policies struggle to meet the diverse needs of different types of higher education institutions. The research also highlights significant areas for improvement in resource allocation, support system construction, and personalized mental health services. Some policies fail to address regional disparities, with weaker implementation in underdeveloped areas and insufficient safeguard mechanisms. In the future, long-term planning for policy optimization is expected, in which priority would be given to localized resource allocation and close collaboration among schools, families, and society to ensure the comprehensiveness and sustainability of mental health education services. The research has identified certain strengths and weaknesses in the policies concerning mental health education for college students in China, thus providing theoretical references and specific recommendations that can be effectively implemented in higher education institutions.

In the information age, the development of big data, cloud computing and other technologies and their penetration in the field of education provide convenience for teaching work, but also have a certain impact on students' mental health. With the rapid development of Internet information technology, In the Internet age, the network environment has a profound impact on college students. Although students can obtain learning resources from the Internet and engage in thought-sparking with others, information that is both good and bad can also have a negative impact on students' mental health. The students have relatively strong self-awareness, weak discriminating ability, and lack of social experience, so network information may change the spiritual inner world of students. For those students with poor self-control ability, the Internet will greatly affect their mental health and make students develop wrong values. These students have radical utilitarian values, vulgar moral choices, and excessive interpersonal communication. To this end, Research on Mental Health Education of College Students Based on Information Technology is very important, teachers must strengthen the mental health education for college students in the Internet age, do a good job of online mental health education, guide students to form correct values and lifestyles, and carry out targeted educational activities to prevent and alleviate students' psychological problems. Based on this, this article will discuss some views on how to do a good job of mental health education for college students on the basis of analyzing the current state of mental health of college students in vocational colleges and the status quo of mental health education in schools.

In order to solve the problem of more and more vicious events caused by college students“ mental ill-health, a research method of health education knowledge service information system model based on virtual reality is proposed. The platform development approach is based on virtual reality to provide technical support to the process of acquiring, maintaining, pushing, applying, and updating the knowledge of mental health education for college students. The system includes a knowledge base for storing college students’ mental health education knowledge, tools for supporting acquisition, classification, editing, and storage of knowledge, and tools for knowledge sharing, application, and innovation of teachers and experts engaged in college students’ mental health education. The results are as follows: after using this system, the proportion of students with excellent mental health status in a university has increased significantly, reaching 75%, and the proportion of students with poor health status has decreased from 15% to 7%. The mental health knowledge sharing platform designed in this paper has obtained satisfactory evaluation in the trial universities, accounting for 68% of the trial population. The system presented in this article demonstrates that the system can significantly improve the mental health of college students, and the system outlined in this document is important for improving the efficiency and quality of mental health education for college students and for effectively managing educational knowledge.

Paying attention to the mental health education of college students, optimizing the psychological quality of college students, and improving their mental health level are inevitable requirements of higher education facing the world. In recent years, the mental health education of college students has always been a hot issue in the education circle, which has attracted much attention. It is the main research purpose of this paper to correctly understand the problems existing in college students’ mental health education and to find reasonable countermeasures and approaches to solve these problems. This paper collects and organizes the dimension facts twice, conducts descriptive analysis, unbiased pattern t-test, chi-square test, variance evaluation, and SNK-q check on the legitimate data, and analyzes the change and impact of the mental health level of students with negative psychological symptoms after 2 years. We use machine learning methods to model and analyze susceptibility factors. Among the psychological susceptibility factors, the UPI scores of students with negative psychological symptoms with different levels of self-esteem, psychological resilience, depressive cognition, positive coping style, negative coping style, different family functions, and ability to perceive social support have significant differences. The mental health level of college students with negative psychological symptoms decreased after 2 years. The self-esteem stage decreased, and the psychological elasticity stage decreased; the longer the cognitive stage of depression, the worse the coping style; the more serious the impairment of family function, the lower the possibility of social support, the more likely it is to lead to psychological problems. After preprocessing the original data, the features of various types of information of the intelligent model are extracted. The test and data analysis results show that the improved recognition accuracy based on the intelligent model is 82.5%, which is higher than the traditional model, which proves the effectiveness and feasibility of the scheme. Using item or dimension data, the model established by machine learning method based on susceptibility factors can effectively predict the changes of mental health of college students with negative psychological symptoms after 2 years, and can effectively identify college students with psychological problems after 2 years.

It is true that with society entering a new era, the pressure of competition for employment of college students is increasing, and society has higher requirements for the quality of high-tech talents, which can result in mental health issues for college students. Therefore, focusing on psychological education for college students and improving their psychological quality is essential. The analysis and countermeasure research on psychological characteristics has become crucial in this context. To address this issue, this work proposes an Improved Particle Swarm Optimization (IPSO) Deep Belief Network (DBN) method to analyze the psychological characteristics of college students. The proposed method first uses DBN to analyze the psychological characteristics of college students. Secondly, the learning factors and inertia weights in PSO are dynamically adjusted to construct IPSO to address the problem of slow overall convergence speed caused by the initial randomization of weights in the DBN training process. IPSO is then combined with DBN to adjust the initial weights for DBN training. This work also proposes a series of countermeasures from different aspects to strengthen mental health education for college students. The validity of IPSO-DBN for college students’ psychological characteristics analysis is verified through comprehensive and systematic experiments, and the feasibility of college students’ mental health education countermeasures is confirmed via comparison. Overall, this work provides an innovative and effective approach for analyzing college students’ psychological characteristics and developing countermeasures to improve their mental health. It shows the potential of combining artificial intelligence with psychological education and highlights the importance of strengthening psychological health education for college students.

Understanding the interests and needs of online health education for college students, exploring the concepts, procedures, models, and approaches of health plans, learning about mental health online, and developing a comprehensive online mental health education plan for as many college students as possible are always very popular. Based on the development and support of online mental health education for students, from the perspectives of teachers and college students, this paper investigates online mental health education for college students in a city, combined with data interviews and Delphi routes; through electronic questionnaires, 900 college students and 30 students were selected by faculty members to conduct an investigation. With descriptive statistics, reliability and availability, chi-square tests, and other options, this paper examines data on student Internet usage and current status and we have the Psychiatry Network. The results show that 91.75% of college students study online every day and 61.72% spend more than 4 hours online every day. Satisfaction with the Internet has a certain impact on the online application and use of the health college education. Students' online motivations and behaviors are multicultural. In order to build a mental health education network, educators believe that the primary goal should be to improve the mental health of college students.

The mental health of college students is a growing concern, especially in light of the COVID-19 pandemic. This thesis investigates the role of open online psychological courses in addressing the mental health challenges confronted by college students. Using the most recent National Mental Health Development Report (2021-2022) by the Chinese Academy of Sciences, which emphasises a high prevalence of depression among this demographic, this study investigates the effect of such courses on the mental health of students. The research objectives include an examination of the utilisation patterns of open online psychological courses, an evaluation of the mental health outcomes associated with these courses, and an investigation of the barriers preventing student participation. It also investigates how educational institutions address the mental health requirements of their students in view of the persistent psychological effects of the pandemic. The majority of college students find open online psychological courses beneficial for mental health enhancement. However, significant obstacles such as schedule constraints, financial constraints, and course quality concerns remain. In addition, the study highlights the potential for these courses to incorporate mental health support into the larger educational curriculum. This research has significant implications for policymakers, educational institutions, and mental health professionals, as it provides insights into strategies for enhancing college students' mental health support and education. By resolving these challenges and leveraging the benefits of open online courses, institutions can nurture a more resilient and psychologically healthy student population.

Faced with the constantly emerging problems of mental health among college students, in order to comprehensively understand the current situation and development needs of college students’ mental health, many educators and researchers propose to study the comprehensive evaluation effect of mental health, in order to judge the changes in college students’ mental health and its role in optimizing educational management. Under the influence of educational ethics concepts, this paper uses ensemble learning algorithms to comprehensively evaluate and predict the mental health of college students, and explores the optimization of educational management. The first aspect is investigating the development and changes in mental health education for college students in recent years and conducting data analysis on the relevant factors involved in the comprehensive assessment of mental health. There is also a need to reorganize the current psychological crisis among college students and the problems faced in optimizing educational management. The second aspect relates to extracting mental health test data after data cleaning and standardized processing, using ensemble learning algorithms for multiple rounds of iterative training to improve data processing efficiency, and establishing a comprehensive prediction model for mental health. This model must be used to validate the algorithm on randomly selected student mental health test data. By using normalization methods and ensemble learning algorithms for sustainable partitioning, the comprehensive evaluation model is optimized to achieve a combination of qualitative and quantitative data analysis. Last aspect is using the integrated learning algorithm to handle imbalanced data and optimize education management. The comprehensive assessment data of college students’ mental health must be incorporated into the optimization of educational management to enhance the effectiveness of management optimization. The research results indicate that ensemble learning algorithms under the influence of educational ethics have good efficacy in processing comprehensive assessment data of college students’ mental health, and can provide reliable assistance for optimizing educational management.

In the new era, the mental health of college students has become the focus of attention. College counselors play an irreplaceable role in the mental health education of college students. Therefore, on the basis of the analysis of the current situation of college students' psychology, this paper discusses the advantages and conditions of college counselors' participation in mental health education from the perspective of college counselors who have the most direct and close contact with students, and tries to explore the role of college counselors in college students' mental health education.

The purpose of this paper is to explore the construction of a long-term mechanism for college students’ mental health education from the perspective of home-school-community collaboration. By analyzing the current situation and challenges of mental health education for college students, this study proposes a set of systematic solutions from the three dimensions of family, school, and society. As the first classroom for individual growth, the education method and atmosphere of the family have a profound impact on the mental health of college students. Schools are responsible for professional education and psychological intervention and need to establish a sound mental health education system and crisis intervention mechanism. At the social level, by strengthening policy guidance, community service, and media publicity, strong support for college students’ mental health education can be formed. Through case analysis and empirical research, this study verifies the effectiveness of the home-school-community collaboration model and puts forward specific suggestions on how to further optimize the mechanism and improve the educational effect. The results show that the close cooperation between home, school, and community is the key to building a long-term mechanism for college students’ mental health education, which is of great significance for promoting the all-round development of college students and maintaining social stability.

This study investigates the usage of painting art therapy in the psychological survey, screening, psychological consultation, psychological crisis intervention and treatment of specific psychological problems, among others, of college students. It also delves into the application status and advantages of painting art therapy in the mental health education of college students for the promotion and application of painting art therapy in China. An online search for related literature on painting art therapy in PubMed, ScienceDirect, EBSCO database and China Academic Journals was conducted using the keywords ‘art therapy’, ‘painting’, ‘emotion’, ‘psychological survey’, ‘psychological screening’, ‘psychological consultation’ and ‘crisis intervention’, which were searched individually or in combination. A total of 25 articles were cited for review. Painting art therapy was found to be widely used in all aspects of college students’ mental health education. It has a high degree of acceptance, has the unique advantages of non-verbal therapy and is very effective. Educators should use painting art therapy not only for addressing the mental health of students but also apply this therapy to other areas of Education Psychology. Keywords: college student; m ental health education ; art painting therapy ; psychological; intervention

The advent of the big data era brings opportunities and challenges to the innovation of mental health education for college students in higher education institutions. In this paper, we monitor the mental health of college students in higher education institutions by collecting students’ consumption data, access control data, network data and historical achievement data, and use a convolutional neural network to extract features from behavioral data and then input the feature set into random forest model for training. The recognition effect of the RF algorithm is analyzed by comparing it with other algorithms, and the problems of college students’ mental health education are analyzed based on the correlation of features. The accuracy of identifying mental health problems of college students based on RF analysis of big data reached 0.72, the recall rate reached 0.58, and the F1-Measure was 0.67. For the correlation of different features, the most significant correlation effect was the time spent in the dormitory on rest days and the number of swiping access cards, with the correlation coefficients reaching 0.2719 and -0.2191, respectively. The analysis based on big data can accurately grasp the current situation and problems of mental health education of college students in higher education institutions and promote the comprehensive development of mental health education of college students.

Psychological education is beneficial in maintaining the psychological health of college students, resolving psychological issues, and creating a harmonious campus and society. Mental health education for college students is a development-oriented basic education activity that contributes significantly to educational quality. To improve the accuracy of college students' mental health assessments, a model for mental health education for college students based on fine-grained parallel computing programming is proposed. This study uses a deep learning algorithm to subdivide the classification of an emotion dictionary, which can be classified by adding negative word lists, polarity conversion dictionaries, and online dictionaries, among other things, based on the theory of ecological instantaneous evaluation. It can be used for both multiclass and detailed emotion analysis. The model is more accurate in assessing the mental health of college students, according to the results of the study. The current emotional state of users can be identified, as well as signs of psychological risk, using emotional analysis of Weibo data, which will become a valuable resource for users seeking clinical psychological consultation in the future.