Psychological crisis warning of international students based on deep learning and computational mathematics
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Le Liu, Hao Zhang, Chengzhu Li, Hao Cui, Wenwen Zhang
With the rapid development of education for overseas students in China, psychological problems caused by cross-cultural factors have become more and more prominent. Based on the psychological questionnaire data, in order to make full use of the relationship information between students, this paper proposes a psychological crisis individual identification model based on bipartite graph convolution network model (B-GCN) to make up for the shortcomings of traditional identification methods. Because the GCN model can not realize inductive learning, this paper improves the GCN model based on the structural characteristics of psychological tests, and proposes a bipartite graph neural network model. The model can classify the students who have never appeared in the graph structure, so as to realize the early warning of a psychological crisis. Experiments demonstrate that the proposed B-GCN model has a good performance.
Graph CNN; Psychological crisis; International students; Deep learning