Existing pedestrian re-identification algorithms heavily rely on convolutional neural networks as the backbone, which often leads to an overemphasis on regions with prominent features while neglecting broader foreground features. This results in insufficiently rich global feature representations and inadequate attention to subtle discriminative features. To address these issues, we propose a feature-enhanced pedestrian ReID algorithm. The global branch utilizes position encoding and a multi-layer, multi-head attention structure to better leverage spatial context information, enhance the understanding of relative spatial positions, and effectively capture spatial structural information, thereby improving feature representation and global feature extraction capability. The local branch optimizes spatial attention using feature matrices associated with spatial vectors, enabling the capture of more compact general appearance features. Furthermore, by modeling the relationships between different channels, it strengthens feature expression in the channel dimension, highlighting distinctive features and improving the attention to discriminative characteristics. Finally, the model is trained using softmax loss, triplet loss, and center loss on the Market-1501 and DukeMTMC-ReID datasets. Experimental results demonstrate the effectiveness and superior performance of the proposed algorithm.