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College students are the most active, most sensitive, and most prone group with respect to various psychological problems in contemporary society. In recent years, with the intensification of social competition, including various pressures such as studies, examinations, economic loss, emotional loss, and employment, the incidence of anxiety, depression and suicide rates has increased. To effectively pay attention to the psychological development of college students and to strengthen mental health education, this research proposes a method to automatically identify the anxiety of college students using a Takagi-Sugeno-Kang (TSK) fuzzy system and deep features. First, preprocess the collected EEG of college students. Secondly, use convolutional neural network (CNN) to extract deep features from the input data. Finally, TSK fuzzy system is used to classify features to obtain the final recognition result. Through experiments on standard data sets and self-made data sets, the experimental results verify the superiority of the anxiety identification method used in this study. The experimental results further demonstrate that the depth features have richer information than traditional features. The noise immunity of TSK fuzzy system makes it show good classification performance and generalization. The recognition results can quickly locate students with anxiety disorders and narrow the scope of investigation for students with psychological problems. The automatic recognition of college students' anxiety can improve the efficiency of schools and teachers in investigating students' psychological problems. This research has very good practical application value.

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features