Abstract: In this paper, we propose an adversarial unsupervised domain adaptation image classification method  based on contrastive learning(CADA), which aims to extend the model trained from a well-labeled source domain to an unlabeled target domain while maintaining good generalization performance. In the previous adversarial unsupervised domain adaptive methods, it simply aligns the features of the source domains and target domains globally, while ignoring whether features belonging to the same class are aligned when two domains are aligned globally and it also doesn't take full advantage of the unlabeled target domain sample. In this paper, the idea of contrastive learning is introduced into the adversarial unsupervised domain adaptation image classification method. By constantly narrowing the distance of similar samples in the target domain in the feature space, and constantly pushing away the dissimilar samples, the classification boundary of the samples in the target domain without labels is clearer, so that the source domain and target domain samples can be aligned globally as well as within the class. The target domain samples after data augmentation are sent into the contrastive learning module, which makes the unlabeled samples of the target domain more fully utilized. Compared with the original adversarial unsupervised domain adaptation method, the average accuracy of the proposed CADA on three data sets, such as Office-31, is about 2%-6% higher than that of the original methods.