A hyperspectral image classification method based on residual generative adversarial network (GAN) is proposed to address the problems of high demand for labeled samples and high classification accuracy in the process of hyperspectral image classification. The method is based on GAN and includes: replacing the deconvolution layer network structure of the generator with an eight-layer residual network composed of an upsampling layer and a convolution layer to improve data generation ability; improving feature extraction ability, the discriminator's convolutional layer network structure is replaced with a thirty-four-layer residual convolutional network. The experiment compares the datasets from Indian Pines, Pavia University, and Salinas. The proposed method is compared to GAN, CAE-SVM, 2DCNN, 3DCNN, and ResNet. The results demonstrate that the proposed method improves overall classification accuracy, average classification accuracy, and Kappa coefficient significantly. Among them, the overall classification accuracy reached 98.84% on the Indian Pines dataset, which is 2.99 percentage points, 22.03 percentage points, 12.91 percentage points, 4.99 percentage points, and 1.79 percentage points higher than the comparison methods. In summary, adding a residual structure to the network improves information exchange between the shallow and deep networks, extracts deep features of the hyperspectral image, and improves hyperspectral image classification accuracy.