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Generated adversarial net GAN is powerful,but it has some disadvantages such as slow convergence, unstable training, and insufficient sample diversity.This paper presents a conditional gradient Wasserstein generation confrontation network model CDCWGAN-GP by Combining the advantage of conditional deep convolution adversarial net CDCGAN and Wasserstein generated adversarial net with gradient penalty WGAN-GP. Using the Wasserstein distance training against the network with gradient penalty guarantees training stability and faster convergence, while adding condition c to guide data generation. In addition, in order to enhance the ability of the discriminator to extract features, the paper designs a global discriminator and a local discriminator to score together, and finally extracts the discriminator for image recognition. The result of simulation experiments show that this method effectively improves the accuracy of image recognition.