Identifying pedestrians under heavy occlusion (Occluded Re-ID) remains highly challenging, primarily because obstacles inevitably corrupt human structural integrity and induce severe spatial-semantic mismatching. Current approaches either struggle to recover fragmented topological features or blindly trust fragile pose estimators, making them highly vulnerable to complex background interference. To overcome these bottlenecks, we present textbf{SSGA}, a unified multi-modal enhancement framework that seamlessly couples topology restoration, cross-modal feature calibration, and semantic-driven decoding. Specifically, a Spatial Guided Graph Convolutional Network (SG-GCN) is first formulated to repair corrupted local structures by embedding physical spatial constraints into visual patch representations. Moreover, to tackle cross-modal mismatching, we propose the Spatio-Semantic Dual-Metric Greedy Alignment (SSDA) strategy. By anchoring visual embeddings to reliable skeletal cues under strict geometric boundaries, SSDA effectively eliminates semantic ambiguity such as symmetrical limb confusion. Furthermore, a Geometry-Aware Semantic Matching (GASM) module is designed to employ learnable semantic queries for dynamically extracting part-level features, which forces the network to highlight visible body regions and filter out occlusion noise. Comprehensive evaluations across five standard benchmarks validate the superiority of our SSGA framework, which establishes new state-of-the-art results and yields substantial improvements particularly on the severely occluded Occluded-Duke and Occluded-ReID datasets.