Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education
With the rapid development of curricula, a large number of studies are emerging to assist in the development of curricula. But in an information society, in the face of rapid learning and increased life expectancy, students face the pressure not to forget; the mental health status as a result of our...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/6394707 |
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| author | Wanting Zheng |
| author_facet | Wanting Zheng |
| author_sort | Wanting Zheng |
| collection | DOAJ |
| description | With the rapid development of curricula, a large number of studies are emerging to assist in the development of curricula. But in an information society, in the face of rapid learning and increased life expectancy, students face the pressure not to forget; the mental health status as a result of our curricula is closely related to our learning. The research and application of the integration algorithm plays an important role in the analysis of the mental health education system. The purpose of this work is to study the application analysis algorithm in the students’ context. This work applies the integration analysis algorithm to students’ mental health analysis and identifies students’ mental health problems using the integration analysis algorithm so that students are well informed and guided. Based on the system engineering method, using the data mining clustering method, a detailed analysis and research on the mental health of college students is done. In this work, a method of student behavior analysis and statistical tools are used to collect mental health data to find common features of different groups of students, in order to better visualize and investigate the mental health of these students on a scientific basis. The results of this study are as follows: a general analysis algorithm application on the analysis of students’ mental health education system allows for an effective understanding of scientific data. FCM and FCM algorithms based on the density of information entropy characteristics were used to investigate the effect of mental health factors on the results of the study and the practicality of the algorithm used, which provided an effective method for the prevention of student mental problems. Assisting the school in formulating corresponding new methods of early prevention and intervention of college students’ psychological disorders will create a good and healthy atmosphere for college students’ study and life. The research results provide a reliable basis for managing and cultivating students. |
| format | Article |
| id | doaj-art-e2fcd11d65384e3c98b5c129f53ab0b3 |
| institution | OA Journals |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-e2fcd11d65384e3c98b5c129f53ab0b32025-08-20T02:21:14ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/6394707Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health EducationWanting Zheng0School of Clinical Medical TechnologyWith the rapid development of curricula, a large number of studies are emerging to assist in the development of curricula. But in an information society, in the face of rapid learning and increased life expectancy, students face the pressure not to forget; the mental health status as a result of our curricula is closely related to our learning. The research and application of the integration algorithm plays an important role in the analysis of the mental health education system. The purpose of this work is to study the application analysis algorithm in the students’ context. This work applies the integration analysis algorithm to students’ mental health analysis and identifies students’ mental health problems using the integration analysis algorithm so that students are well informed and guided. Based on the system engineering method, using the data mining clustering method, a detailed analysis and research on the mental health of college students is done. In this work, a method of student behavior analysis and statistical tools are used to collect mental health data to find common features of different groups of students, in order to better visualize and investigate the mental health of these students on a scientific basis. The results of this study are as follows: a general analysis algorithm application on the analysis of students’ mental health education system allows for an effective understanding of scientific data. FCM and FCM algorithms based on the density of information entropy characteristics were used to investigate the effect of mental health factors on the results of the study and the practicality of the algorithm used, which provided an effective method for the prevention of student mental problems. Assisting the school in formulating corresponding new methods of early prevention and intervention of college students’ psychological disorders will create a good and healthy atmosphere for college students’ study and life. The research results provide a reliable basis for managing and cultivating students.http://dx.doi.org/10.1155/2022/6394707 |
| spellingShingle | Wanting Zheng Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education Applied Bionics and Biomechanics |
| title | Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education |
| title_full | Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education |
| title_fullStr | Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education |
| title_full_unstemmed | Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education |
| title_short | Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education |
| title_sort | cluster analysis algorithm in the analysis of college students mental health education |
| url | http://dx.doi.org/10.1155/2022/6394707 |
| work_keys_str_mv | AT wantingzheng clusteranalysisalgorithmintheanalysisofcollegestudentsmentalhealtheducation |