Abstract: In order to solve the problem that fake reviews are rampant on the Internet, but there is no fully open Chinese data set for Chinese fake reviews detection in the field of fake reviews research, a Chinese fake reviews data generation model based on generative adversarial network is proposed. Firstly, Monte Carlo search is used to obtain a batch of samples from the text sequence generated by the generator. Then, the feedback of discriminator, classifier and reconstructor is converted into reward scores by reinforcement learning. Finally, reward scores back to the generator, and the parameters of the generator are optimized to generate fake review data with corresponding class tag attributes and features close to the real world. The BLEU value is used as the evaluation index. Experimental results show that on the dataset of this paper, the proposed generative model achieves better BLEU values and achieves a high level of performance.