To address the issue of insufficient spatial correlation information for emotion EEG characterization extracted by shallow graph convolution, this paper proposes a deep graph convolutional network model. The model utilizes deep graph convolution to learn the intrinsic relationships among global channels of emotional EEG, applying residual connections and weight self-mapping during the convolutional propagation process to address the problem of node features in deep graph convolution networks converging to a fixed space and failing to learn effective features. Additionally, PN regularization is added after the convolutional layer to expand the distance between different emotional features and improve emotion recognition performance. Experimental results on the SEED dataset show that compared to shallow graph convolution networks, the accuracy of the proposed model has increased by 0.7% while the standard deviation has decreased by 3.15. These results demonstrate the effectiveness of the global brain region spatial correlation information extracted by this model for emotion recognition.