Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Abstract

With the development of society, psychological health becomes a basic standard for a college student to grow into a qualified person. This study is aimed at using data mining principles and methods to excavate the factors that lead to psychological problems of college students, to purposefully carry out psychological interventions, use visual art design methods to promote college students’ psychological health treatment, and build a perfect system of college students’ psychological treatment. Based on the theories of data mining, we built a data analysis model, elaborated the data preprocessing method, and applied the Apriori algorithm to analyze the data of obsessive-compulsive symptoms and interpersonal sensitivity symptoms, and various psychological problem attributes extracted the strong association rules and analyzed the results. Take advantage of the corresponding unique school environment and educational advantages, to build a set of mental health education methods suitable for modern college students, and truly enable them to obtain satisfactory psychological interventions in a reasonable art design treatment phase. Based on the above association pattern mining results, a series of preprocessing operations were performed on the data, and then, the Apriori algorithm was applied to discover the potential association relationships among 9 psychological dimension factors of college students, and then, the ID3 decision tree algorithm was used to construct a decision tree and pruning process, from which the classification rules of students’ psychological problems were analyzed and discovered. These studies provide some practical reference basis for school counseling work.