Analysis and Research of Psychological Crisis Behavior Model Based on Improved Apriori Algorithm

Abstract

A psychological crisis occurs when a person experiences extreme stress and finds traditional coping strategies inadequate. Apriori algorithms mining collections of frequently used goods and associated rules. The apriori technique is usually used on a database with thousands or millions of transactions. Since diverse mental health phases have steadily drawn the interest of all segments of society, modeling college students’ damaging psychological crises derives the core of college students’ psychotherapy from positivist and interpretive grounds. A mental health assessment system is offered to solve the high misevaluation rate and poor work efficiency of the existing college students’ mental health assessment method. Hence, in the proposed method, data mining (DM) enabled BP neural network (DM-BPNN), which integrates psychological crisis in apriori algorithm to overcome the challenges mentioned above of the psychological turmoil in the college student’s mental health and increase their performance. BPNN supports mental health management by proposing solutions to college students’ psychological crises. These solutions include setting up an early alert system with cross-link information and a group of people educated in mental health. Furthermore, a DM-based mental health evaluation is needed to improve the college student mental health assessment procedure. Student mental health evaluation and DM summary based on data collected from student surveys and processed using the Apriori algorithm. A DM-BPNN effectively predicts the rise in the psychological crisis behavior model based on an improved apriori algorithm.

Keywords:

• Psychological crisis
• apriori algorithm
• college students
• mental health
• data mining
• BP neural network

Author’s contributions

All authors contributed to the design and methodology of this study, the assessment of the outcomes, and the writing of the manuscript.

Disclosure statement

There is no conflict of interest among the author(s).

Data availability statement

All data generated or analyzed during this study are included in the manuscript.

Additional information

Funding

This work was supported by the project “The ideological and political research project of the Wenzhou education bureau: Research on the Construction Path of College Students’ Psychological Crisis Early Warning System under the Background of Big Data”. Project Number: WGSZ202225.

Notes on contributors

Yiping Yan

Yiping Yan belongs to Nanping city, Fujian, China. She has completed Postgraduate. Her research area is mental health education.