Vehicle re-identification (Re-ID) aims to accurately identify and associate the same vehicle across different camera views to achieve cross-regional, continuous tracking and trajectory analysis. However, existing Re-ID methods often suffer from insufficient feature representation and poor robustness in complex scenarios due to camera viewpoint variations, occlusions and high intra-class similarity. To address these challenges, this study proposes a dual-branch network fusing regional features with dynamic side information. This study design a regional contextual association module (RCAM) using multi-head attention to adaptively refine key local features, and a dynamic side information embedding (D-SIE) mechanism to flexibly incorporate camera and viewpoint data. By employing CutMix augmentation, the model′s robustness and generalization are significantly enhanced. On VeRi-776, the method achieves 83.6% mAP and 97.5% Rank-1; on the 800 subset of the VehicleID dataset, the Rank-1 accuracy reaches 85.4% and the Rank-5 accuracy hits 97.8%. Cross-domain experiments also verify the strong generalization performance of the model. Experimental results demonstrate that the model proposed in this paper significantly improves the model robustness and fine-grained feature discrimination capability for the vehicle re-identification task.