Abstract: The current deep convolutional neural network models applied to coal-rock image recognition have problems such as large volume and cumbersome calculation process. It is difficult to meet real-time detection requirements, and it has poor adaptability to complex environments such as low lighting and high dust. In order to solve the above problems, a coal-rock image recognition method integrating drilling geological information is proposed. Firstly, the improved spectral residual saliency detection (ISRSD) algorithm is used to enhance the quality of coal-rock images, effectively reducing the adverse effects of complex environments on the features of coal-rock images. Secondly, the method uses the attentional VGG (AVGG) deep convolutional neural network model. The AVGG performs pruning based on VGG, adds convolutional block attention module (CBAM), and introduces adaptive learning rate adjustment strategy to efficiently extract coal-rock image features. Finally, the Bayesian model is used to integrate the features of coal-rock images with the geological information obtained from the borehole geological column chart, in order to improve the accuracy and robustness of coal-rock classification. The experimental results show that the image enhanced by the ISRSD algorithm has more prominent targets, lower color distortion, and relatively complete preservation of image features such as edges and textures. The accuracy of the AVGG model is comparable to that of the VGG model, but the average inference time, parameter count, and model size are only 15.61%, 33.44%, and 33.40% of the VGG model, respectively. Compared with using only the AVGG model to recognize coal-rock images, using the Bayesian model to fuse drilling geological information improves accuracy by 1.85%, reaching 97.31%.