Hybrid Mechanism of Deep Q-Network (DQN) and Graph Mining Algorithm in “Six-Dimensional Integrated” Mental Health Assessment and Prediction: Taking Hefei University of Economics as an Example

Abstract

How to evaluate the mental health status of college students? How to establish evaluation indicators for the mental health status of college students? How to predict mental health status? These are important issues faced by mental health education in universities. In response to the above issues, the article proposed a hybrid mechanism based on Deep Q-Network (DQN) and graph mining algorithm, and applied it to the “Six-dimensional Integrated” mental health assessment and prediction at Hefei University of Economics. The experimental results show that the prediction method based on Deep Q-Network has the highest accuracy of 98%, the highest recall rate of 88%, and the highest stability of 96%. From this, it can be seen that the proposed mixed mechanism of DQN and graph mining can provide effective evaluation and prediction for the mental health status of college students.