A hyperspectral image classification method based on superpixel segmentation and the convolutional neural network (CNN) is proposed to address the issues of low utilization of spatial-spectral features and low classification efficiency of CNN in hyperspectral image classification. First, the first three principal components were filtered after extracting the first 12 image components utilizing the principal component analysis (PCA), and the three filtered bands were then subjected to superpixel segmentation. Sample points were then mapped within the hyperpixels, enabling it to select superpixels rather than pixels as the basic taxon. Finally, the CNN was used for image segmentation. Experiments on two public datasets, WHU-Hi-Longkou and WHU-Hi-HongHu, show improved accuracy obtained by combining spatial-spectral features compared to using only spectral information, with classification accuracy of 99.45% and 97.60%, respectively.