Research on Psychological Emotion Recognition of College Students Based on Deep Learning

Abstract

College students’ anxiety, depression, inferiority complex, interpersonal sensitivity, and a series of mental health problems have a very serious negative impact on individuals, families, and society. In order to obtain better psychological emotion recognition effect of college students, this paper proposes a psychological emotion recognition algorithm based on multisource data. One-dimensional convolutional neural network (1D-CNN) was used to mine students’ online patterns from online behavior sequences. According to the consumption data of students in the canteen, abnormal scores are calculated to depict the dietary differences among students. At the same time, the students’ psychological state data provided by the psychological center are used as labels to improve the shortcomings of the questionnaire. Five kinds of common classification algorithms are trained by training set, and the classifier with the best effect is selected through evaluation of verification set, which is used to identify students with mental health problems in the test set. Experimental results show that precision, recall, and F1-measure reach 0.68, 0.56, and 0.67, respectively. 75% of students with mental health problems are identified. The psychological and emotional recognition system of college students based on deep learning provides systematic method and theoretical support for the school to find students with psychological problems in time and provide intervention.