A Method of Abnormal Psychological Recognition for Students in Mobile Physical Education Based on Data Mining

At present, the abnormal psychological recognition of middle school students is mainly through psychological questionnaire, after data statistical processing, to assess whether there is abnormal psychological students. The recognition accuracy is strongly dependent on the reliability of the questionnaire, which leads to the poor recognition accuracy and stability. In order to solve these problems, the method of abnormal psychological recognition of students in mobile PE teaching based on data mining will be studied. After analyzing the influence of PE teaching on students’ psychology, the behavioral characteristics that represent students’ psychology are extracted. After constructing the students’ psychological view, the students are classified preliminarily. Through constructing mental state mining decision tree, using iForest algorithm to realize abnormal mental recognition for middle school students. The test results of recognition method show that the accuracy of the mental recognition method is stable between 87.28% and 87.95%, and the recognition reliability is higher.