Unsupervised person re-identification(Re-ID)aims to improve the model\'s scalability and ob-tain 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 im-prove the performance of unsupervised person re-identi-fication.The multi-granularity feature extraction module integrates global and local information of different granu-larity to obtain the multi-granularity person feature rep-resentation with rich discriminative information.The source domain classification module learns the labeled source dataset classification and obtains the person\'s dis-criminative knowledge in the source domain.On this basis,the domain adaptive module further considers the difference between the target domain and the source do-main 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.