An improved Fisher’s criterion-based deep convolutional generative adversarial network algorithm （FDCGAN） is proposed in this study to solve the problem of quality deterioration in generated images when the training sample size is insufficient or number of iterations decreases. In this method， a linear layer is added to the discriminative model to extract category information. Then， Fisher’s criterion is used in backpropagation to combine label and category information. To minimize errors， the weights are adjusted iteratively while maintaining small intra-class and large inter-class distances such that the weights can rapidly approach the optimal value. A comparison of the experimental results of the FDCGAN model with that of the most recent six network models shows that the proposed model achieves better performance in all the FID metrics. In addition， applying the proposed model to the current advanced models in generalization tests yields more satisfactory results.