Prediction of College Students' Psychological Crisis with a Neural Network Optimized by Harmony Search Algorithm


  • Zhiping Jia Hebei Chemical & Pharmaceutical College



backpropagation neural network (BPNN), warning of college students’ psychological crisis, partial least squares (PLS) method


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.




How to Cite

Jia, Z. . (2022). Prediction of College Students’ Psychological Crisis with a Neural Network Optimized by Harmony Search Algorithm. International Journal of Emerging Technologies in Learning (iJET), 17(02), pp. 59–75.