Generative adversarial networks (GANs) effectively solve the difficulty of obtaining image data, but are disadvantaged by unstable training and poor quality of the generated images. To resolve these problems, this paper proposes an image generation method for an improved deep convolution GAN based on residual structures. The proposed method uses the residual structure to deepen the network and combines the image-label information to obtain the deep-level features of real image samples. It also introduces spectral constraints into the discriminator model, thereby improving the training stability of the network and the effective generation of the image data. The experimental results show that the proposed method has better performance in the visual effect and objective evaluation of the generated images.