To address the problem of a small accepting neighborhood range during the node embedding of traditional graph convolutional networks, this study proposes a hyperspectral image classification network based on an improved GraphSAGE algorithm. Firstly, the original image is preprocessed by using the super-pixel segmentation algorithm to reduce the number of image nodes. This not only conserves the local topology information of the original image to the largest extent but also reduces algorithm complexity and thus shortened operation time. Secondly, the average sampling of the target node is carried out by the improved GraphSAGE algorithm, and the neighbor nodes are aggregated by the average aggregation function to reduce spatial complexity. Finally, the proposes approach is compared with other models on the public hyperspectral image datasets Pavia University and Kenndy Space Center. The experiment proves that the hyperspectral image classification network based on the improved GraphSAGE algorithm can achieve good classification results.