<p>Recent advancements in attention-based techniques have significantly propelled the field of person re-identification. Despite this progress, the challenge of accurately retrieving individuals across multiple camera views remains a substantial obstacle. Current person re-identification methods predominantly emphasize effective feature extraction and the suppression of irrelevant information through local or global attention mechanisms. However, these approaches often fail to fully exploit the potential of spatial relational information from a holistic perspective. The interdependence between feature nodes across distinct regions indicates the presence of spatial relational knowledge, which can significantly enhance the fuzzy inference of semantic relevance and attention, especially in alignment-constrained settings. This paper introduces a novel approach that simultaneously models both “local–global” relationships and human body topology to better capture relational information. Specifically, we propose a learning paradigm that leverages non-local attention to model relationships among different body regions, thereby improving the model’s ability to capture semantic correlations between spatial regions. Experimental evaluations on several benchmark datasets demonstrate the superiority of our method, which not only achieves substantial performance gains but also outperforms existing state-of-the-art approaches. Additionally, our comprehensive ablation studies further affirm the efficacy and advantages of the proposed framework.</p>