Cross-domain person Re-identification (re-ID) has witnessed rapid development, driven by the breakthrough advancements of unsupervised techniques in visual tasks. Current unsupervised domain adaptation (UDA) methods generally follow a two-step strategy, which involves data generation and feature extraction, are widely adopted in cross-domain person re-ID. However, the accuracy of UDA networks remains limited, primarily due to the weak feature alignment capability, which fails to generate high-quality pseudo target domain data, and the networks’ tendency to over-emphasize global features while neglecting crucial local characteristics during training. To address these issues, we propose a UDA-based Multi-Scale and Dual-Branch Network (MSDBNet), which integrates a Style-Injected Generative Adversarial Network (SIGAN) and a Dual-Branch Alignment and Cross-Attention Fusion Network (DBCF-Net). Specifically, SIGAN alleviates domain distribution discrepancies through a multi-scale domain-aware generation strategy. DBCF-Net mitigates critical information oversight through dual-branch global-local feature alignment, graph sampling optimization, and cross-attention fusion learning. Extensive experiments on two public datasets and a self-built dataset demonstrate the superiority of MSDBNet, achieving improvements of up to 0.7% mAP and 1.2% Rank-1 accuracy compared with existing leading networks.