Unsupervised　person　re-identification　(Re-ID)　is　more　substantial　than　the　supervised　one　because　it　does　not　require　any　labeled　samples.　Currently,　the　most　advanced　unsupervised　Re-ID　models　generate　pseudo-labels　to　group　images　into　different　clusters　and　then　establish　a　memory　bank　to　calculate　contrastive　loss　between　instances　and　clusters.　This　framework　has　been　proven　to　be　remarkably　efficient　for　unsupervised　person　Re-ID　tasks.　However,　clustering　operation　inevitably　produces　misclassification,　which　brings　noises　and　difficulties　to　contrastive　learning　and　affects　the　initialization　and　updating　of　the　prototype　features　stored　in　the　memory　bank.　To　solve　this　problem,　we　propose　a　new　robust　unsupervised　person　Re-ID　model　with　two　developed　modules:　Cluster　Sample　Aggregation　module　(CSA)　and　Hard　Positive　Sampling　strategy　(HPS).　The　CSA　module　aggregates　each　sample　in　the　same　cluster　through　the　multi-head　self-attention　mechanism.　This　process　enables　the　initialization　of　prototypes　based　on　the　similarities　observed　within　clusters.　Additionally,　the　HPS　strategy　extracts　the　dispersion　degree　of　each　sample　by　means　of　a　self-attention　aggregation　module　(SAA)　that　has　been　trained　by　CSA　module.　According　to　the　obtained　indicators,　the　hardest　positive　sample　is　sampled　to　update　the　prototype　feature　stored　in　the　memory　bank.　With　the　self-attention　mechanism　fusing　the　information　among　instances　in　each　cluster,　the　implicit　relationships　between　samples　can　be　better　explored　in　a　more　refined　way.　Experiments　show　that　our　method　achieves　state-of-the-art　results　against　existing　unsupervised　baselines　on　Market-1501,　PersonX,　and　MSMT17　datasets.