The images in coal mines have problems of dim, blurry and unclear edges. To address these issues, this article proposes a lightweight mine image super-resolution reconstruction method that fuses hierarchical features and attention mechanism. Firstly, by integrating the coordinate attention mechanism into the residual block, this article designs a residual coordinate attention module, which enables the network to obtain rich high-frequency detailed information. Secondly, the hierarchical feature fusion mechanism is adopted to fuse the feature map information of different network levels. Thereby, the reconstruction of edge detail information is promoted. Finally, the dimensionality reduction is performed on the fused features to reduce the amount of model computation and parameters. In addition, to make the proposed model have better generalization performance in real-mine scenes, a coal mine underground image dataset CMUID is constructed for the training and testing experiments of the network model. Experimental results demonstrate that the reconstructed image quality of the proposed algorithm is superior to other comparison algorithms in both objective indicators and subjective feelings. Compared with the OISR algorithm on the underground coal mine image data set, when the scaling factor is set to 4, the average values of PSNR and SSIM of the proposed algorithm can be improved by 0. 318 5 dB and 0. 012 6. As for the public data set, the average PSNR and SSIM of the proposed algorithm are also improved by 0. 1 dB and 0. 003 5, respectively, as well as the number of network model parameters is reduced by 70. 7% .