Occluded person Re-identification (Re-ID) is to identify a particular person when the person’s body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. In this paper, we propose a novel Attention Map-Driven Network (AMD-Net) for occluded person Re-ID. In AMD-Net, human parsing labels are introduced to supervise the generation of partial attention maps, while we suggest a Spatial-frequency Interaction Module (SIM) to complement the higher-order semantic information from the frequency domain. Furthermore, we propose a Taylor-inspired Feature Filter (TFF) for mitigating background disturbance and extracting fine-grained features. Moreover, we also design a part-soft triplet loss, which is robust to non-discriminative body partial features. Experimental results on Occluded-Duke, Occluded-Reid, Market-1501, and Duke-MTMC datasets show that our method outperforms existing state-of-the-art methods. The code is available at: https://github.com/ISCLab-Bistu/SA-ReID.