Unsupervised person re-identification has attracted more and more attention due to its extensive practical application prospects. Most clustering-based contrastive learning methods treat each cluster as a pseudo-identity class, overlooking intra-class variances caused by differences in camera styles. While some methods have introduced camera-aware contrastive learning by partitioning a single cluster into multiple sub-clusters based on camera views, they are susceptible to misguidance from noisy pseudo-labels. To address this issue, we first refine pseudo-labels by leveraging the similarity between instances in the feature space, using a weighted combination of the nearest neighboring predicted labels and the original clustering results. Subsequently, it dynamically associates instances with possible category centers based on refined pseudo-labels while eliminating potential false negative samples. This method enhances the selection mechanism for positive and negative samples in camera-aware contrastive learning, effectively mitigating the influence of noisy pseudo-labels on the contrastive learning task. On Market-1501, MSMT17 and Personx datasets, mAP/Rank-1 reached 85.2%/94.4%, 44.3%/74.1% and 88.7%/95.9%.