To address the problem of unsatisfactory classification results due to the limited number of labeled samples and insufficient extraction of diverse features in hyperspectral image classification tasks, this paper proposes a hyperspectral image classification method based on three-dimensional dilated convolution and graph convolution. Firstly, we introduce different scales of dilated convolution (DC) to build a three-dimensional dilated convolution network model to extract multi-scale deep spatial-spectral features. Secondly, we build a graph convolution neural network model by aggregating the neighborhood feature information of graph nodes to obtain the contextual features with spatial structure. Finally, to improve the representation capability of diverse features, we fuse deep spatial-spectral features with spatial contextual features and use Softmax to achieve classification. The proposed method can make full use of the diverse features of hyperspectral images and has a strong feature learning capability, which can effectively improve the classification accuracy. The proposed method is experimentally compared with seven related methods on the hyperspectral datasets of Indian Pines and Pavia University, and the results show that the proposed method could obtain optimal results with an overall classification accuracy of 99.33% and 99.41%.