Electroencephalogram (EEG) has become an important indicator reflecting emotions. Due to its natural graph structure characteristics, it has made significant progress in the emotional recognition using graph convolutional networks (GCN). However, existing methods face limitations: (1) insufficient integration of psychological prior knowledge, limiting the utilization of brain activity patterns, and (2) simplistic node relationship construction, neglecting the universality and functional connectivity of brain regions. Therefore, we propose a dual-path parallel graph convolutional network (DP-GCN). The first path leverages psychological prior knowledge to segment electrodes into brain regions and employs an attention mechanism to integrate features. The second approach employs a data-driven method, using a sparse stacked autoencoder to reconstruct brain region features, while a learnable, input-independent adjacency matrix captures EEG patterns associated with emotions. Finally, a cross-attention mechanism integrates features from both paths. DP-GCN has been evaluated on public dataset, achieving an accuracy of 82.69%±4.16%, demonstrating its competitive performance.