To minimize the performance difference between neural machine translation (NMT) and human translation and solve the problem of insufficient training corpora, this study proposes an improved NMT method based on the generative adversarial network (GAN). First, the sentence sequence of the target end is added with small noise interference, and then the original sentence is restored by the encoder to form a new sequence. Secondly, the results of the encoder are presented to the discriminator and decoder for further processing. In the training process, the discriminator and the bilingual evaluation understudy (BLEU) objective function are employed to evaluate the generated sentences, and the results are fed back to the generator to instruct its learning and optimization. The experimental results demonstrate that compared with the traditional NMT model, the GAN-based model greatly improves the generalization ability and translation accuracy of the model.