Optimization of College Students’ Mental Health Education Based on Improved Intelligent Recognition Model

Abstract

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.