As one of the important methods of sample generation, the generative adversarial network(GAN) can generate samples based on the data distribution in any given data set, but it has generated sample texture blur, unstable training process and mode collapse in the actual training process. In order to solve the above problems, this paper proposes a residual generative adversarial network,(RGAN) based on the deep convolutional generative adversarial network(DCGAN) and the residual network. The sample generation method uses the residual network and the convolution network to construct the generator and the discriminator respectively, and combines the learning optimization strategy of the positive and negative sample fusion training to optimize the training process. Among them, the depth residual network can recover the rich image texture and, the positive and negative sample fusion training can increase the robustness against the network and effectively alleviate the instability of network training and the collapse of the model. This paper designs several simulation experiments on the 102 category flower dataset. The experimental results show that the RGAN can effectively improve the quality of generated samples.