Recent unsupervised person re-identification studies have used clustering and memory dictionaries for pseudo labels to train models. However， these studies ignore that the datasets of person re-identification are collected by different cameras， that is， the distribution difference between cameras is large， and a larger camera variance will lead to decrease in model accuracy. Therefore， camera cluster contrast learning is proposed， which includes cluster contrast loss and camera contrast loss. The cluster contrast loss can realize the consistent update of memory dictionary and reduce the influence of noise labels on the model. Camera contrast loss reduces camera variance by building camera cluster center for each cluster in each camera， narrowing the camera cluster center distance of the same cluster， and making different camera cluster centers farther apart. By camera cluster contrast learning， the impact of camera variance and noise labels on the model is reduced， and the performance of person re-identification is improved. On the four public datasets， camera cluster contrast learning has shown excellent results， effectively alleviating the impact of camera variance on the model.