Neural network-based college students' psychological behavior abnormal change monitoring and early warning method

Abstract

The invention provides a neural network-based college students' psychological behavior abnormal change monitoring and early warning method. The method includes the following steps that: 1) the multi-information source data of students are acquired, a data pre-processing algorithm is adopted to obtain the psychological behavior data of the students on the basis of the multi-information source data,the depression psychological test self-evaluation forms of the students are obtained, the psychological health states of the students are labeled with labels through the results of the psychologicaltest self-evaluation forms, and features related to the psychological health states are extracted through the psychological behavior data, and a PCA algorithm is adopted to extract the main feature components of the data through the features; 2) after the psychological behavior data of the students are obtained, and the main feature components are extracted, and a neural network algorithm is utilized to establish and train a depression psychological early warning model; and 3) the multi-information source data of new students are obtained, and the depression states of the new student individuals are evaluated according to the depression psychological early warning model.

Description

Technical field [0001] The invention relates to a monitoring and early warning method, in particular to a monitoring and early warning method of college students' psychological behavior abnormality, and belongs to the field of big data application. Background technique [0002] According to a report from the World Health Organization, depression has an incidence of about 11% worldwide, and has become the fourth most harmful disease to human health. By 2020, it may become the second most serious disease after heart disease. In our country, the incidence of depression is as high as 7%, and the treatment rate is only 20% due to untimely discovery and insufficient understanding. Suicide deaths due to depression are frequent. As an active and sensitive special group in social life, college students are in a young age when their physiology and psychology are undergoing tremendous changes. Mental health problems are more prominent than other groups. The 2014 University of California, B...

AI Technical Summary

Problems solved by technology

The advantage is that it eliminates the influence of subjective factors on the assessment of mental health status. The disadvantage is that the source of behavioral data is single, the mining is not thorough enough, and the accuracy of mental health status assessment cannot be guaranteed.

[0007] To sum up, although mental health research based on big data technology has received some attention, existing efforts and research have not yet established a theoretical model to describe human psychology and behavior. Problems such as incomplete content and incomplete data mining are essentially still in the static evaluation stage of mental health status

[0008] So far, no patents have been found that use educational big data and machine learning algorithms to establish a monitoring and early warning model for psychological behavior abnormalities

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