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.