Unsupervised domain adaptation （UDA） methods leverage global feature distribution matching to realize knowledge transfer from source domain to target domain， while ignoring fine-grained local instance information. An unsupervised person re-identification method based on two-tiered domain adaptation （TTDA） is proposed， in which the omni-scale network（OSNet） is selected as the backbone network， and global feature distribution matching and localized instance matching between source and target domains are performed jointly in an end-to-end deep learning framework. And in order to effectively mine transferable useful knowledge from associations of different pedestrian IDs between source and target domains， the cross-domain adaptability is improved with a knowledge selection mechanism. Experimental results on multiple large-scale public datasets show that compared with other state-of-the-art methods， the proposed method achieves significant improvements in terms of mean average precision （mAP） and top-k hit rate for unsupervised cross-domain person re-identification tasks.