Abstract: The rock thin−section images contain a large amount of geological feature information that cannot be observed with the naked eye. The lithology identification of rock thin−section images lays the foundation for subsequent oil exploration and production. Aiming at the problems of unbalanced lithology identification data set and many identification model parameters, an improved lightweight MobileViT model is proposed to model and analyze the rock slice images covering more than 90% of common lithology. First, to enable the model to better learn the unique features contained in each type of rock slice image, adding numbers of the dataset set is performed on the image. Secondly, use GELU to replace the ReLU6 of the MV2 module in MobileViT as the activation function of the module, which effectively solves the problem of neuron death and improves the convergence speed of the model. Finally, the training set and the test set are divided, the cosine annealing algorithm is used to automatically update the learning rate, and the transfer learning is used to speed up the training process, so as to realize the automatic identification of rock slice images. The experimental results show that the accuracy of the improved MobileViT for lithology identification is 82.8%, and the model parameters are only 7.66M, which has good robustness.