Abstract: In the entity alignment task, the accuracy of alignment is disturbed due to the lack of labels for noisy entity pairs in the entity alignment task. Robust Entity Alignment (REA) method is proposed, and noise sensing entity alignment module and noise detection module are designed. The noise sensing entity alignment module is a knowledge map encoder based on Graph Convolutional Networks (GCN), which updates and embeds entity pairs in the knowledge map. The noise detection module designs a noise generator and a noise discriminator based on the Generic Adversary Networks (GAN) to distinguish the noise entity pairs in the entity pairs. Finally, an interactive reinforcement training strategy is used to combine iterative noise perception with entity alignment. The experimental results show that the new method can effectively improve the accuracy of entity alignment in the case of noise when tested on the DBP15K dataset. Compared with GCN Align and IPTransE benchmark embedded models Hits@1、Hits@5 and M_RR evaluation indicators have been greatly improved.