Emotion Analysis of College Students Using a Fuzzy Support Vector Machine

With the rapid development of society, the number of college students in our country is on the rise. College students are under pressure due to challenges from the society, school, and family, but they cannot find a suitable solution. As a result, the psychological problems of college students are diversified and complicated. The mental health problem of college students is becoming more and more serious, which requires urgent attention. This article realizes the monitoring of university mental health by identifying and analyzing the emotions of college students. This article uses EEG to determine the emotional state of college students. First, feature extraction is performed on different rhythm data of EEG, and then a fuzzy support vector machine (FSVM) is used for classification. Finally, a decision fusion mechanism based on the D-S evidence combination theory is used to fuse the classification results and output the final emotion recognition results. The contribution of this research is mainly in three aspects. One is the use of multiple features, which improves the efficiency of data use; the other is the use of a fuzzy support vector machine classifier with higher noise resistance, and the recognition rate of the model is better. The third is that the decision fusion mechanism based on the D-S evidence combination theory takes into account the classification results of each feature, and the classification results assist each other and integrate organically. The experiment compares emotion recognition based on single rhythm, multirhythm combination, and multirhythm fusion. The experimental results fully prove that the proposed emotion recognition method can effectively improve the recognition efficiency. It has a good practical value in the emotion recognition of college students.