In view of the lack of ore data set and ore classification and recognition model, a lightweight ore classification model algorithm based on improved mobilenet V2 is proposed, which takes the ore image imaged by X-ray irradiation as the data set and mobilenet V2 as the main network. Firstly, by adjusting the expansion factor and width factor, the amount of model parameters is greatly reduced to realize the purpose of model lightweight; Secondly, the efficient channel attention mechanism is embedded in some inverse residual modules and the original model classifier, and the residual inverse residual module is replaced by a parallel feature extraction network with deep hole convolution, so as to enhance the ability of model feature information extraction and improve the accuracy of model recognition; Finally, the training method of transfer learning is used to initialize the weight and accelerate the model training. After improvement, the ore recognition accuracy of the algorithm is improved to 96.720%. Compared with vgg16, googlenet, xception, shufflenet and mobilenet V2, the accuracy and ore detection speed have been improved. In general, compared with other algorithms in this experiment, the improved algorithm has better performance for ore recognition.