This paper analyzes the factors that affect the mental health of college students, and the focus of this analysis is on emotional-emotional factors. The extroverted presentation of emotional affects is used as visual information to study the mental health status of college students. Based on the advantages of long and short memory neural networks based on deep learning models in processing two-dimensional images, a computer vision task is used to perform visual recognition, target detection, and expression image classification of college students’ facial expressions. The use of video facial expression recognition with multi-feature fusion is utilized to effectively identify the facial expression machine of college students in both laboratory-controlled and outdoor environments. The mental health status of college students was analyzed in terms of facial expression recognition and feature extraction. The recognition rate for general features was 80.3%, 89.3% for six specific facial emotions, and 84.4% for LBP features.