An Assessment and Analysis Model of Psychological Health of College Students Based on Convolutional Neural Networks

Abstract

Psychological health assessment and psychological problem identification essentially belong to problems of pattern recognition or nonlinear classification; its system contains complex nonlinear interactions among various factors, having basic characteristics of multivariable, multilevel, and strong coupling. An important problem in the field of artificial intelligence solved by convolutional neural networks (CNN) is to simplify complex problems, minimize the number of parameters, and thus greatly improve the algorithm’s performance. Therefore, CNN has outstanding advantages in establishing the assessment and analysis model of college students’ psychological health. This study determined the psychological health standards of college students, selected measurement tools for college students’ psychological state, elaborated the principles of psychological assessment based on text information, performed the sample set data establishment and data processing of the assessment and analysis model of psychological health, conducted network establishment, training, and simulation, carried out a case experiment and its result analysis, explored the cause analysis of college students’ psychological health problems, and finally discussed the prevention and intervention of college students’ psychological problems. The study results show that the input and output of the CNN-based assessment and analysis model of college students’ psychological health are their evaluation data and assessment results, respectively, and the optimal hyperparameters of the model are determined through fold cross-validation analysis to improve the model’s over-fitting problem. After the training is completed, the model can predict the changes in college students’ psychological state in the future through the psychological test data. The CNN uses supervised machine learning method to construct an assessment and analysis model of college students’ psychological health, and establishes the mapping relationship between college students’ personal background and their psychological health. The network error continuously adjusts network connection weight according to gradient descent algorithm to minimize its error, so that the convolutional layer and the pooling layer can learn the optimized feature expression of the input data.