A deep learning-based prediction model of college students’ psychological problem categories for post-epidemic era—Taking college students in Jiangsu Province, China as an example

Abstract

For a long time, it takes a lot of time and energy for psychological workers to classify the psychological problems of college students. In order to quickly and efficiently understand the common psychological problems of college students in the region for real-time analysis in the post-epidemic era, 2,000 college students’ psychological problems were selected as research data in the community question section of the “Su Xin” application, a psychological self-help and mutual aid platform for college students in Jiangsu Province. First, word segmentation, removal of stop words, establishment of word vectors, etc. were used for the preprocessing of research data. Secondly, it was divided into 9 common psychological problems by LDA clustering analysis, which also combined with previous researches. Thirdly, the text information was processed into word vectors and transferred to the Attention-Based Bidirectional Long Short-Term Memory Networks (AB-LSTM). The experimental results showed that the proposed model has a higher test accuracy of 78% compared with other models.