Analysis and Prediction of Students’ Psychological Characteristics of Law Breaking and Crime Based on Support Vector Machine Algorithm

Abstract

Abstract In recent years, some people with high intelligence and high quality have violated the law and even committed crimes, which has ruined their bright future. It is really sad. A school is a place where teenagers learn skills and improve their quality. It should be a pure land. The frequent occurrence of juvenile students’ crimes seriously affects the normal teaching order and endangers the safety of students’ lives and property. With the rapid development of support vector machines (Abbreviation: SVM), it has also become one of the main data fusion technologies. SVM is a creative machine learning method based on statistical learning theory. It is a learning method specialized in finite sample prediction. This paper studies the psychological characteristics of students’ crimes based on the SVM algorithm. From the aspect of proportion, the result of the SVM algorithm is ideal, and the proportion of the training set is up to 33.35%. The above algorithm has good expansibility for such sample data, but when the parameters are not appropriate, the proportion is only more than 20.12%, which once again explains the importance of the SVM algorithm. Only through the SVM algorithm to improve the efforts of the whole society, can we put an end to the generation of criminal psychology of higher vocational students from the source and cultivate more high-quality talents for the country.