The invention discloses a fine-grained image classification method based on a generative adversarial network and an attention network, and the method comprises the steps: determining an image classification category, and building a training image set of a corresponding category; designing a deep attention convolution network for image fine-grained classification, wherein the network comprises fourparts, namely a VGG16 full convolution layer, SS attention region generation, a spatial pyramid pooling layer ROI pooling layer and an overall and local feature combined classification full connection layer; designing a structure of a generative adversarial network DAC-GAN, a generative network and a discrimination network; training a DAC-GAN network by using the training sample set, and storinga discriminant network model; and carrying out classification prediction on the image types by using the discrimination network model. According to the invention, the accuracy of the image classification network is improved, and the problem of insufficient data of a small sample size is solved.