LAUSR

Ideological education is an important part of students’ education. Good ideological education can greatly reduce students’ mental health problems. Based on relevant theories of psychology, this study analyzes how psychological crises can be warned against through continuous observation of emotions. Further, a psychological crisis warning model is built based on education big data, providing innovative observation methods and ideas for warning college students about psychological crises. The core idea of the model is that stress events are the external cause and personality and mood changes are the internal causes. The calculation, based on the evaluation of stress events and personality, can draw on different types of emotions and emotional threshold intensities to judge emotions. At the same time, the evaluation is based on time sequences of mood changes to judge the psychological crises that college students face by the level of risk. Combining psychological knowledge and machine learning methods, this study proposes a psychological crisis warning algorithm based on educational data, which predicts the duration and intensity of emotions by calculating stressful events and emotional attenuation. The simulation results show that the proposed algorithm can reflect the emotional changes of college students when they are subjected to stress events, and the effectiveness of the proposed algorithm is preliminarily verified. We conducted timely psychological intervention for the students who received negative stimuli, and the results show that timely psychological intervention and ideological education support are necessary and helpful.