Emotion is closely related to healthy and abnormal mood is the alarm of our body. This paper is concentrated on the objective and accurate emotion classification using EEG signal. We propose emotional patches and combine it with the deep belief network(DBN) to achieve high-precision emotion classification. DBN is able to fit the distribution of the EEG signal and mapping the extracted feature to the higher-level characteristics space where we can easily perform high-precision classification. Compared with the other method, our method uses the emotional patches which have considered the temporal information of emotion and reduce the influence of noise. In addition, our model doesn't need to be trained twice to complete higher classification accuracy. We divide the EEG signal and choose the vital β frequency band where we perform feature extraction. Based on the SJTU Emotion EEG Dataset(SEED), we perform the emotion classification experiment and compare our method with the commonly used classifiers such as SVM, LR and CCA ect. The experimental result demonstrates that our method achieves the highest classification accuracy and outperform the state-of-theart emotion classification approaches based on EEG.