Domain adaptation, as a systematic framework for addressing data set migration and adaptation, has grown rapidly in recent years. After the emergence of the generative adversarial network (GAN), the introduction of adversarial ideas has brought new ideas to the unsupervised adaptation problem in the domain adaptation. This paper compares and analyzes the intrinsic relationship between GAN and domain adaptation, and then generates an improved GAN method. A domain adaptation method combining self-attention layer is proposed to improve the defect that  long-distance dependence cannot be modeled. At the same time, considering the difference in GAN and domain adaptation tasks, this paper improves the self-attention layer by introducing new learning parameters, so that it has higher precision and robustness in classification tasks. Finally, the effectiveness and feasibility of the proposed algorithm are verified by experiments on the open field adaptive dataset.