Article Details

Title · Authors · Abstract · Keywords · DOI · References

Research on Psychological Early Warning Model for Vocational College Students Based on Nonlinear SVM

CHEN Feng

(Taizhou Vocational College of Science and Technology, Taizhou 318020, China)

Section：Information Application · Vol. 2025, Issue 9 · Pages：162-166,171 · 2096-4706（2025）09-0162-06 · CLC number：TP391 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.09.030

Abstract

This research addresses the psychological health issues of vocational college students, collects multi-dimensional data on students' psychological states, personal information, and life behaviors, and uses the nonlinear SVM algorithm to construct a psychological early warning model. The model can effectively classify and identify student psychological crises into four levels of severe psychological crises, general psychological issues, potential psychological distress, and no psychological distress. The empirical research design includes data collection and preprocessing, feature weight normalization processing, and data analysis. The results show that characteristics such as psychological state, family composition, and family economic status have a significant impact on student psychological crises. The SVM model achieves an early warning accuracy of up to 93.5%, which is superior to models such as Multilayer Perceptron and Random Forest, providing a practical tool for mental health screening and intervention among vocational college students.

Keywords

early warning model; colleges and universities; psychological health; SVM algorithm

References

  1. [1] Wu Ran, Wang Yujing, Chen Jiangyuan The dilemma and response of home school communication in psychological crisis intervention in universities [J]. Chinese School Health, 2017, 38 (1): 106-108
  2. [2] Wang Jisheng, Xu Duoyong, Tang Li, etc Construction of Intelligent Early Warning Model for Psychological Crisis of College Students Based on Big Data [J]. Journal of Chengdu Medical College, 2024, 19 (1): 111-115
  3. [3] Wang Shan, Wang Huiju, Qin Xiong faction, etc Architecture of Big Data: Challenges, Current Status, and Prospects [J]. Journal of Computer Science, 2011, 34 (10): 1741-1752
  4. [4] Liu Fengjuan A review of research on the application of big data in education [J]. Modern Educational Technology, 2014, 24 (8): 13-19
  5. [5] Li Yonghui Research on the intervention effect of trait group psychological counseling on college students' test anxiety [J]. Chinese Journal of Clinical Psychology, 2019, 27 (1): 206-209+142
  6. [6] Zhao Dan Research and Application of a Decision Tree based Psychological Crisis Warning Model for College Students [D]. Beijing: Beijing Forestry University, 2020
  7. [7] GODDARD H，SHAMIR L. SVMnet: Non-Parametric Image Classification Based on Convolutional Ensembles of Support Vector Machines for Small Training Sets [J].IEEE Access，2022，10：24029-24038.
  8. [8] NALEPA J，KAWULOK M. Selecting Training Sets for Support Vector Machines: A Review [J].Artificial Intelligence Review，2019，52（2）：857-900.
  9. [9] Fan Wenrong Research on Depression Tendency Early Warning Model for College Students Based on Social Platform Data [D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023
  10. [10] Luosha On the Modern Transformation of Psychological Crisis Intervention for College Students [J]. Ideological and Theoretical Education, 2020 (1): 107-111
  11. [11] Hu Yiqiu, Liu Zhenghua Intervention study on mental health of depressed college students: differential effects of different types of school support [J]. Journal of Education Science, Hunan Normal University, 2019, 18 (5): 120-125

About the authors

Chen Feng (1995-), male, Han ethnicity, from Taizhou, Zhejiang, assistant lecturer, master's degree, research direction: data mining.