Targeting the issue of insufficient accuracy of hyperspectral image classification methods, a hyperspectral image classification method based on Spatial-spatial transformer (SST) network is proposed. Firstly, the hyperspectral images are preprocessed into one-dimensional feature vectors. Then, the SST hyperspectral image classification network with spectral-spatial attention module and pooled residual module is designed. The overall classification accuracy of the proposed classification method on Indian Pines dataset and Pavia University dataset is 98.67% and 99.87%, respectively, which indicates that this method has high classification accuracy and provides a new scheme for hyperspectral image classification and application.