Open-world person re-identification (ReID) faces severe spatially asymmetric interferences, such as partial occlusion and illumination distortion. Existing models adopting static weight fusion irreversibly corrupt identity representations when processing localized noise. To address this, we propose a Scene-Prompt-Driven Dynamic Routing Expert Network (SPDR-Net). Guided by spatial topological prior constraints, SPDR-Net treats person semantic parts as independent local experts. First, a semantic expert module decouples and refines local features using human spatial topology priors. Second, a Prompt-Guided Dynamic Routing (PGDR) network extracts high-level scene contexts to dynamically evaluate each expert’s reliability, assigning routing weights to attenuate noise propagation. Finally, a global–local fusion module superimposes high-purity local features onto a global identity anchor, maintaining topological integrity to generate a unified descriptor for robust person re-identification. Extensive experiments on seven public benchmarks and a newly constructed real-world street dataset (SD-ReID) demonstrate that SPDR-Net achieves highly competitive performance against state-of-the-art methods and exhibits superior robustness under severe spatial-asymmetric interferences. Furthermore, end-to-end multi-camera closed-loop tests verify its robust decision-making capability and high engineering application value in real-world security systems.