Prediction And Analysis Of Mental Health Status Of Graduate Students Based On Neural Network

With the high speed development of knowledge and economy, the requirement of the society for talents is growing, the amount of graduate student is increasing rapidly, the competition pressure of each graduate students is getting more and more, which seriously affects the postgraduates' mental health. In the graduate student population, because of suffering from mental illness, which leads to be unable to complete their studies and affects their own development, this phenomenon is endless. However, our country's concern about the psychological health of the group is relatively weak at present. Therefore, it is necessary to study the mental health status of graduate students at this stage.This paper expands evaluation factor and carries on the judgment of graduate student mental health condition by studying the traditional mental health scale researchand combinating our country student population's present situation and the characteristic. Through analyzing the main influencing factors of mental health of graduate students in our country,and collect corresponding data and the neural network model was used to predict the mental health status of graduate students. This paper mainly has completed the following several aspects of the work:(1) Basing on in the existing measuring methods SCL-90 table of nine factors add the account type, family composition and whether only child for a total of 12 factors as related factors, establish the factors set of the mental health status of students of fuzzy comprehensive evaluation model, using iterative method to calculate the membership degree, the evaluation model are more applicable in our research group.(2) Make the main influence factors of students' mental health status as input sample,establish the optimized BP neural network of postgraduates' mental health prediction model,the corresponding fuzzy comprehensive evaluation of the results are the output sample, usingneural network self-learning function of the network learns, and get the mapping relationship between the various factors and their psychological health status which is able to predict the mental health of graduate students.(3) Collect the data of one university graduate students, through the self-learning of sample data and the model building by the neural network has a good prediction result, and take the practical application for several students, and compare with the UPI college students personalitytest. The results show that the predicted model can predict the Chinese graduate students mental health status better.