Abstract: The existing machine vision coal and gangue recognition algorithms have small sensing field, low feature extraction ability and slow training convergence speed. In order to solve these problems, an intelligent coal and gangue identification algorithm embedded in dilated convolution and batch normalization module was proposed. This algorithm used dilated convolution to replace the 3×3 convolution kernel in VGGNet16 network with dilated convolution to increase the receptive field and improve the feature extraction capability of the network. At the same time, a batch normalization module was embedded between the convolutional layer and the activation layer to avoid the disappearance of the gradient and accelerate the convergence rate of model training. The experimental equipment was used to collect coal and gangue images, make coal and gangue image data sets, train the model, and evaluate the training and prediction effect of the model based on FLOPs and F1 scores. The experimental results show that the FLOPs of the improved algorithm is 71 632 538 times, and the F1 score of the test set is 0.994 3. The training converges in the fifth cycle and the accuracy is above 97%. The comparison with the training results of other network models further shows that the proposed model has faster convergence speed and better prediction effect.