Lithological identification and classification constitute indispensable facets of geology, resource exploration, and related disciplines. The emergence of hyperspectral remote sensing has ushered in novel perspectives for lithological identification. The utilization of machine learning to extract information from hyperspectral rock images, thereby enabling accurate lithological identification, holds paramount practical significance. Currently, the application of machine learning methods for the classification of hyperspectral rock images lacks a comprehensive exploitation of spatial and spectral information. Therefore, this paper introduces a three-dimensional convolutional residual network structure augmented with an attention mechanism, capable of effectively extrac-ting spatial, spectral, and joint spatial-spectral features from hyperspectral rock images. In this experiment, images of 10 different types of rock samples were collected using a drone equipped with a hyperspectral sensor. The algorithm proposed in this study was applied to classify hyperspectral rock images. Experimental results indicate that, in comparison to traditional machine learning algorithms such as SVM and RF, as well as deep learning algorithms like ResNet, 3DCNN, and SSRN, the proposed algorithm exhibits higher accuracy.