Abstract
Despite the growing concern with students’ mental health education, there are neither extensive research on stressors of psychological crisis, nor targeted research on the psychological crisis warning of college students with different levels of psychological crisis. To solve the problem, this paper aims to explore the psychological crisis warning and physical education (PE) intervention of college students based on artificial neural network (ANN). Firstly, the important evaluation indexes were determined for psychological crisis warning of college students; according to the adverse reactions and performance of college students in physiology, cognition, emotion, and behavior, the index data were processed by partial least squares (PLS) method. Next, a psychological crisis warning model was developed based on the optimized ANN: the harmony search (HS) algorithm was improved based on the differential evolutionary (DE) algorithm, and then used to optimize the backpropagation neural network (BPNN). The proposed model was proved feasible and effective through experiments.
Highlights
College students generally have to face the triple pressures of school work, employment, and life; when their ideality collides with social reality, their psychology is prone to lose balance and generate psychological crises [1,2,3,4,5,6]
This paper aims to research the early warning of college students' psychological crisis and physical education (PE) intervention based on artificial neural network (ANN), and the main content of this paper includes the following aspects: 1) Take the adverse reactions and performance of college students in physiology, cognition, emotion, and behavior as important evaluation indexes of psychological crisis warning; 2) Process the data of the evaluation indexes based on the partial least squares (PLS) method; 3) Build the psychological crisis warning model based on improved ANN, elaborate on the principles of the improvement and the harmony search (HS) algorithm, and give the execution flow of the backpropagation neural network (BPNN) optimized by the improved algorithm
According to the adverse reactions and performance of college students in physiology, cognition, emotion, and behavior, 23 evaluation indexes were selected for the early warning of psychological crisis; based on the principle of PLS, corresponding programs were written in MATLAB to extract the components of the 23 evaluation indexes
Summary
College students generally have to face the triple pressures of school work, employment, and life; when their ideality collides with social reality, their psychology is prone to lose balance and generate psychological crises [1,2,3,4,5,6]. There’re certain differences in the adverse reactions and performance of the psychological crisis of college students. Starting from the source of stressors and with the help of effective identification methods, we could discover college students with psychological probiJET ‒ Vol 17, No 02, 2022. Huang [18] proposed a psychological crisis warning system based on the C4.5 decision tree algorithm; in the paper, the classification rules generated by the C4.5 decision tree was fully applied based on quantified mental state of students to facilitate the showing of the relationship between students’ mental health indicators and their psychological crisis. Based on a classification of college students’ psychological crises and the nature of crisis events
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