Multivariate Decision Tree-Oriented Early Warning Method for College Students’ Psychological Crisis Behavior

To prevent the occurrence of psychological crisis behavior and help college students grow from the crisis is not only an important issue related to college students’ mental health, but also a very necessary and urgent practical issue related to college students’ family happiness, college talent training, and social harmony and stability. Therefore, a multivariable decision tree-oriented early warning method for college students’ psychological crisis behavior is proposed. Using the laser beam emitted by the sensor as a reference standard, the psychological crisis behavior data is extracted, converted into different formats, divided into linear regions, and used to build a multivariate decision tree model. The model is used as the judgment processing target, and the judgment equation of mental health status data is established. The status data is divided into different processing groups as the processing object, calculate the maximum state value of the state data in the two data combinations to obtain the optimal early warning result, and finally realize the early warning of psychological crisis behavior. The experimental results show that the final success rate of early warning obtained by this method is high, which can improve the efficiency of early warning; the number of missing data sets was the least, and most of the subjects’ psychological risk tolerance was at the medium level.