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The gangue recognition technology is one of the key technologies in the intelligent construction of mines. To address the problem of low accuracy of the gangue recognition model caused by low illumination and high dust environment at the working face and the difficulty of recognizing small target gangue, a coal gangue image recognition model based on CSPNet-YOLOv7 target detection algorithm is proposed. Cross Stage Partial Network (CSPNet) is used to improve the backbone feature extraction network of YOLOv7 model, optimize the gradient information to reduce the network parameters, while Recursive Feature Pyramid (RFP) and Switchable Auto Convolution (SAC) to replace the simple up and down sampling and normal convolution modules in the neck feature extraction network, and to enhance the generalization ability of the network by using three migration training for feature learning of different widths and depths. The experimental results show that the CSPNet-YOLOv7 model has an average accuracy mean of 97.53%, an accuracy rate of 92.24%, a recall rate of 97.91%, an F1 score of 0.95, a model parametric number of 30.85×106, a floating point operation count of 42.15×109, and a frame rate of 24.37 f/s transmitted per second, Compared to the YOLOv7 model, the average mean accuracy is improved by 7.46%, and the number of parameters and floating point operations are reduced by 17.23% and 60.41%, respectively, compared to the FasterRCNN-Resnet50, YOLOv3, YOLOv4, MobileNet V2 -YOLOv4, YOLOv4-VGG, YOLOv5s models. The CSPNet-YOLOv7 model has the highest average accuracy mean for coal gangue identification, while the number of parameters and floating point operations is small, which has a good balance between identification accuracy and speed. Finally, the CSPNet-YOLOv7 model is validated through downhole field tests, providing an effective technical means for accurate coal gangue identification.