The intelligent recognition of graphite is particularly critical to the transformation of the mining industry to informatization and intellectualization. To address the long time and low efficiency in manually identifying graphite, this study proposes an improved AlexNet network for graphite image recognition. First, image preprocessing is performed on the data set through random cropping, horizontal flipping according to probability, and normalization to achieve data augmentation. Then, the activation function ReLU6 is employed to compress the dynamic range so that the algorithm can become more robust. The batch standardization algorithm is used for normalization to speed up the convergence, and the convolution kernel is resized to enhance the generalization ability. Finally, dropout regularization is added to the fully connected layer to further prevent overfitting. Compared with the existing method, the proposed method reduces the loss value and improves the average accuracy of graphite recognition in the simulation experiment.