As security issues have received widespread attention， the research on person re-identification has become more realistic， which is gradually being applied to video surveillance， intelligent security and other fields. The increasing of the number of monitoring equipments provides massive data support for research， but manual labeling or detector recognition inevitably introduces noisy labels. When training large-scale deep neural networks， as the amount of data increases， the noise of the label brings non-negligible damage to model training. In order to solve the noise label problem of person re-identification， this paper combines noise and non-noise data to train differentiated features， and proposes a noise-label adaptive pedestrian re-identification method without using additional verification sets， noise ratio， types and other priors. In addition， the method adaptively learns the weight of noise data to further reduce the influence. On the noisy Market1501 and DukeMTMC-reID data sets， the state of the art is severely affected by noise. The proposed method can improve the evaluation index by about 10% on this basis.