In recent years, significant progress has been made in the classification of hyperspectral images (HSI) based on generative adversarial nets (GAN). Although they can alleviate the problem of limited training sample size, they are easily affected by imbalanced training data and have the problem of pattern collapse. To this end, a SPCA-AD-WGAN model for HSI classification is proposed. Firstly, to address the issue of reduced classification accuracy caused by imbalanced training data, the study adds a separate classifier and trains it separately from the discriminator. Secondly, it introduces the Wasserstein distance into the network to alleviate the GAN model collapse. The experimental results on two HSI datasets indicate that SPCA-AD-WGAN has better classification performance.