Abstract
At present, the existing dynamic early-warning model of college students' psychological crisis cannot obtain psychological characteristics, and the error of psychological crisis prediction is large. The early-warning result of psychological crisis appears big deviation. A new dynamic early-warning model of college students' psychological crisis is designed based on characteristic attributes. In the process of modeling negative psychological crisis of college students, the fusion constraint fuzzy theory obtains the essence of college students' psychology from subjective and objective reasons, gives the different stages of college students' psychology, obtains the corresponding information entropy of the behavior hindrance in each stage of college students' psychology, and calculates the threshold value of the behavior tendency of college students' psychology. Based on the psychological state information of college students obtained by perception, the fuzzy comprehensive early-warning data model of feature attributes is constructed. According to the fuzzy comprehensive early-warning method and the early-warning index system, the early-warning factor set of college students' psychological crisis is constructed, and the dynamic early-warning model of college students' psychological crisis is constructed. Experimental results show that the mining results are consistent with the actual situation, and the grey relational degree, small error probability, and mean square ratio index are better than the present model.
Highlights
With the development of the economy, the introduction of social competition mechanism and the enlargement of the enrollment scale of colleges and universities, the conflicts, and pressures in the fields of ethics, values, behavior patterns, interpersonal relationships, employment, and job hunting of college students are increasing, and the probability of psychological frustration is increasing [1]
Based on the abnormal psychological state of college students’ cluster discovered in the previous experiment, this paper uses the method of six indicators such as psychological adaptability, frustration tolerance, emotional stability, security tendency, hedging ability, and temperament to warn the subjects of the abnormal psychological state cluster of a mental crisis and determines the trend of the overall psychological state of the subjects according to the summary of various indicators
Psychological crisis intervention is the most important link to relieve the psychological pressure of college students and avoid mental illness. erefore, it is important to understand the psychological state of college students in time and predict the psychological crisis as early as possible
Summary
Received 10 December 2021; Revised 30 December 2021; Accepted 18 January 2022; Published 9 February 2022. The existing dynamic early-warning model of college students’ psychological crisis cannot obtain psychological characteristics, and the error of psychological crisis prediction is large. A new dynamic early-warning model of college students’ psychological crisis is designed based on characteristic attributes. In the process of modeling negative psychological crisis of college students, the fusion constraint fuzzy theory obtains the essence of college students’ psychology from subjective and objective reasons, gives the different stages of college students’ psychology, obtains the corresponding information entropy of the behavior hindrance in each stage of college students’ psychology, and calculates the threshold value of the behavior tendency of college students’ psychology. Based on the psychological state information of college students obtained by perception, the fuzzy comprehensive early-warning data model of feature attributes is constructed. According to the fuzzy comprehensive early-warning method and the early-warning index system, the early-warning factor set of college students’ psychological crisis is constructed, and the dynamic early-warning model of college students’ psychological crisis is constructed. Experimental results show that the mining results are consistent with the actual situation, and the grey relational degree, small error probability, and mean square ratio index are better than the present model
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