Application of emotion recognition technology in psychological counseling for college students

Abstract

Abstract In traditional psychological counseling, limitations of patients’ subjective feelings and expressive abilities, as well as the varying levels of professional skills and conversation skills of counselors, make it difficult to properly solve psychological problems. Emotional recognition technology can help psychological counselors better understand the inner world of patients, thereby providing more targeted psychological counseling services for college students. 2D convolutional neural network-long short-term memory (2DCNN-LSTM) and 1D convolutional neural network-long short-term memory network (1DCNN-LSTM) were used for emotion recognition. Wavelet packet coefficient sequences were utilized to analyze multimodal data; multi-core convolutional neural network models were applied for multimodal feature layer fusion, and LSTM recurrent neural networks were used to effectively identify valence and college student emotions. The experimental results showed that under multimodal recognition, the average recognition accuracy of 2DCNN-LSTM for fourth-year university students was 18.7% higher than that of 1DCNN-LSTM for fourth-year university students. Compared with 1DCNN-LSTM, 2DCNN-LSTM can achieve better recognition accuracy. The research results can help psychological counselors better understand the emotional state and needs of students and provide more personalized and targeted counseling services.