Person re-identification, a core pillar of intelligent surveillance and smart city development, often exhibits significant accuracy degradation in real-world scenarios due to occlusion. Existing convolutional neural network-based person re-identification methods are constrained by local receptive fields, making long-range dependency capture across occluded regions difficult. Transformer-based person re-identification methods, despite global modeling capabilities, suffer from insufficient local-global feature fusion, leading to poor robustness in severely occluded scenarios. An end-to-end occluded person re-identification method based on dynamic feature enhancement and hierarchical gated fusion is proposed to tackle these problems. It employs a dynamic feature enhancement module to optimize local details and noise resistance of mid-level features, and a hierarchical multi-scale gated fusion module to mitigate semantic dilution in high-level features, constructing an end-to-end feature processing pipeline of "mid-level enhancement-high-level purification". The proposed method is compared with existing methods on Occluded-Duke, Occluded-ReID, Market1501 and MSMT17 datasets. Experimental results show Rank-1 accuracies of 74.8%, 88.8%, 96.7% and 90.9%, with mAP accuracies of 67.0%, 86.3%, 93.8% and 77.6%, respectively, validating its effectiveness and superiority in occluded scenarios.