The research is carried out for the purpose of emotion recognition, and the signal feature method of wavelet filtering transformation combined with autoregressive model extraction is proposed on the basis of Principal Component Analysis (PCA). Besides, sentiment classification is realized on the basis of gradient promotion classification tree. The focus of feature extraction is laid on the changes of Electro Encephalo Gram (EEG) signals and the changes of wavelet components as features of EEG signals. The multimodal standard database DEAP proposed by Koelstra et al. to analyze human emotional state is adopted to extract eight positive and negative emotions to represent 14 channels of EEG data in each brain region. The results suggest that the average accuracy of the algorithm for 8 kinds of emotions in pairwise classification is 95.76%, and the highest accuracy is 98.75%, making it possible to help emotional recognition.