As an objective and direct source of information， electroencephalogram （EEG） is widely used in the task of emotion recognition. In order to extract the information implicit in the spatial connectivity features of EEG signals， this paper proposes an emotion recognition method based on the spatial connectivity features and residual convolutional neural network （SCF-RCNN） model. In this method， Pearson correlation coefficient （PCC）， phase-locked value （PLV） and mutual information （MI） are extracted from the preprocessed EEG signals as spatial connectivity features， and a convolutional neural network model containing two residual modules is used to extract emotional information. Experimental results on the SEED dataset show that the connection matrix constructed by PLV is more closely related to EEG emotion， with an average accuracy of 93.38% and a standard deviation of 3.35%. Compared with traditional algorithms， SCF-RCNN performs better in classification tasks in the field of emotion recognition， showing its important application potential in the field of emotion recognition.