Deep learning technology is increasingly applied in vehicle license plate recognition. However, when training the model, there is a lack of data under different environments. To address this problem, several different Generative adversarial networks were applied to generate more vehicle license plate data in different environments, including low light environment, fuzzy environment, environment of bad shooting angles and environment of license plate fouling etc. Results showed that generated license plate data by CycleGAN in different environments had a good performance, which closed to real data in style migration. Wasserstein GAN (WGAN) not only the greater stability and high generalization can be achieved, but also the realistic images were produced. Deep Convolution Generative Adversarial Network (DCGAN) also generated real images but it was difficult to train. Generative adversarial networks (GAN) often had the problem of model collapse, so the ideal images cannot be generated. The better confrontation network selected in a more complex environment to extend the data set preprocessing work has great significance to improve the recognition rate of vehicle license plate recognition technology through this research.