Unsupervised　person　re-identification　(Re-ID)　aims　to　improve　the　model＇s　scalability　and　obtain　better　Re-ID　results　in　the　unlabeled　data　domain.　In　this　paper,　we　propose　an　unsupervised　person　Re-ID　method　based　on　multi-granularity　feature　representation　and　domain　adaptive　learning,　which　can　effectively　improve　the　performance　of　unsupervised　person　re-identification.　The　multi-granularity　feature　extraction　module　integrates　global　and　local　information　of　different　granularity　to　obtain　the　multi-granularity　person　feature　representation　with　rich　discriminative　information.　The　source　domain　classification　module　learns　the　labeled　source　dataset　classification　and　obtains　the　person＇s　discriminative　knowledge　in　the　source　domain.　On　this　basis,　the　domain　adaptive　module　further　considers　the　difference　between　the　target　domain　and　the　source　domain　to　learn　adaptively　for　the　model.　Experiments　on　multiple　public　datasets　show　that　the　proposed　method　can　achieve　a　competitive　performance　among　other　state-of-the-art　unsupervised　Re-ID　methods.