To address the issues of style discrepancies between datasets and pseudo-label noise during clustering in unsupervised domain adaptation (UDA) for person re-identification (Re-ID), and to better leverage global and local information, this paper proposes a label mutual optimization-based UDA method. The approach first incorporates a feature enhancement module into the feature extractor to obtain more robust representations. A multi-branch network is then employed to separately extract global and local pedestrian features, with cross-consistency scores calculated to iteratively optimize the network and mitigate the impact of pseudo-label noise. Finally, cluster reliability evaluation criteria are applied in the clustering algorithm to further enhance network performance. Extensive experiments on multiple datasets validate the effectiveness of the proposed method.