Abstract: In view of the large interference ofman-made and environmental factors in the transportation of open-pit mine vehicles, the control of the load is greatly affected by human and environmental factors, and there are problems such as light vehicle ticketing and man-made packages that are not conducive to production management, a method for open-pit mine truck loading condition recognition was proposed based on deep convolution features.By construction of experimental dataset,the deep convolutional features can be extracted from open-pit mine truck loading condition images by using transfer learning AlexNet model,and the automatic recognition of truck loading condition can be realized based on SVM(Support Vector Machine) multi-classification model.Then,open-pit mine fleet workload statistics can be completed on this basis.During the experiment, based on the same set of experimental data sets, the GoogLeNet, ResNet, SqueezeNet, and DenseNet models were subjected to migration learning, and the deep convolution features of the truck loading condition image were extracted, and the same support vector machine multi-classification model was used to automatically recognize the truck loading condition .The results show that under the constraints of space and time resources, the AlexNet model after transfer learning has the best overall performance among the five convolutional neural networks, and the image deep convolution features extracted by it have the highest accuracy in truck loading state recognition. Compared with the traditional manual design of image features, this methodcan better complete the automatic identification task of truck loading status in open-pit mines, and the recognition accuracy of the test data set is more than 97%. On this basis, statistics on the carrying workload of the open-pit mine fleet can effectively identify the actual loading conditions of the open-pit trucks, improve the production efficiency of the ton-kilometers carried by the open-pit trucks, and effectively solve the problem of management and control of the open-pit mine vehicle workload.