Predictive Model and Analysis of Psychological Depression Based on College Students’ Behavior Data Mining

Contemporary college students face all kinds of pressure and are easy to cause psychological problems. In order to make students and schools do a good job in preventing psychological depression, this paper proposes a student depression prediction model based on college students’ behavior data mining. Due to the shortcomings of large error and low reliability of prediction results in the traditional psychological depression prediction model, it is impossible to carry out large-scale psychological depression data analysis. In order to solve the defects of traditional psychological depression prediction model and improve the reliability of psychological depression prediction results, a psychological depression prediction model based on data mining technology is proposed. Firstly, the sensor is used to collect the signals related to psychological depression, and the signals are denoised to obtain high-quality psychological depression signals; then, the features are extracted from the psychological depression signals, and the support vector machine in data mining technology is used to train and learn the relationship between the features and the types of psychological depression, so as to construct the prediction model of psychological depression; finally, the simulation experiment of psychological depression prediction is carried out on MATLAB platform. The results show that the prediction accuracy of psychological depression of the traditional model is less than 85%, the prediction accuracy of psychological depression of the proposed model is more than 90%, and the time of psychological depression prediction modelling is reduced, which can meet the development trend of modern psychological depression prediction and analysis.