Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning

Abstract

College students are easily affected by the outside world, which leads to mental health problems, so it is particularly important to accurately evaluate and analyze the mental health status of college students. At present, the evaluation and analysis model of college students’ mental health is inaccurate and inefficient, which cannot analyze the mental health problems of college students. In order to evaluate and analyze the mental health problems of college students more accurately, this paper designs an evaluation and analysis model of college students’ mental health from the perspective of in-depth learning. The accuracy of model evaluation and analysis is improved, and a better comparison result is obtained. Firstly, the BP neural network model was compared with the logistic model and ARIMA model, and the results showed that the accuracy of the BP neural network model was more than 70% in five comparisons and was higher than that of the logistic model and ARIMA models. Second, the BP deep learning method is compared with several conventional methods (KNN, MF, NCF, and DMF) in the comparison phase of the model. The RMSE, MAE, and MAPE of the BP method are lower than those of the other four traditional methods. Finally, in the comparative experiment, the precision and AUC of the BP model are improved by 2%, and the three indicators of precision, recall, and F1 are also higher than those of other models. Through the specific evaluation of the five indicators of the four college students, from the five indicators of psychological adaptability, frustration, emotional stability, temperament, and personality, the mental health of the four college students is better.

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Retracted: Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”