Design of a neural network model-based system for predicting students’ mental health status and psychological intervention in colleges and universities

At the present stage, the staff of mental health center in colleges and universities have a heavy workload, fatigue work and low work efficiency, and it is urgent to explore new paths to alleviate the severe situation of mental health work in colleges and universities.In this paper, we first start from the students' mental health assessment data and use data mining technology to analyze the students' mental health status.Then, students' behavioral characteristics are digitally represented to construct a prediction model of students' mental health status based on PDNN neural network.Finally, the design method of psychological intervention system in colleges and universities is proposed.In the collected mental health assessment data, the age distribution is skewed toward the younger population, and nearly 55% of these students show a tendency toward psychological abnormality.And the average accuracy and high group recall of the prediction model of students' mental health status established using PDNN neural network were 88.95% and 87.44%, respectively, which verified the feasibility of the modeling method in this paper.Using the psychological intervention system designed based on the method of this paper for the intervention experiments, there is no significant difference between the experimental group using the system and the control group not using the system in the factors before the intervention (p>0.05), while after the intervention the experimental group scored significantly lower than the control group in the total mental health score, interpersonal relationship sensitivity, depression and anxiety factor items.This proves the validity of the intervention system design method in this paper, which can be applied in psychological intervention methods in universities.