Abstract: In order to improve the recognition accuracy of coal vitrinite macerals and performance of recognition model, reduce the manual intervention in the training of recognition model, a lightweight deep learning network model for coal vitrinite submaceral recognition, which is embedded with an improved attention mechanism, is proposed. Firstly, aiming at the problem of small amount of vitrinite sample data, a data enhancement method of random clipping, rotating, mirroring and noise adding is employed to enhance the original sample data and improve the generalization ability of the recognition model. Then, a backbone network is constructed based on the ShuffleNet V2 model pre-trained on a large data set ImageNet. A transfer learning method, which freezes some layers near the input and adjust the weight parameters of layers near the output of the pre-trained model according the enhanced training sample set of vitrinite microscopic image, is employed to generate some deep feature extraction layers. Finally, a channel attention mechanism ECA, which is improved according to the characteristics of distinct texture feature of vitrinite microscopic image, is embedded before the output of the ShuffleNet V2 model, and a new lightweight end-to-end recognition network model is constructed to implement the automatic recognition of seven submacerals of coal vitrinite. The experimental results show that the average recognition accuracy of submacerals with this method can reach 97.85%, gone beyond 5.71% of the original ShuffleNet V2 model. Compared with the classical neural network, the proposed model has higher accuracy, lesser number of parameters and calculation, and rapid convergence velocity. Compared with other lightweight networks and other attention embedding mechanisms, the proposed network not only maintains a less amount of parameters, but also significantly improves the recognition accuracy.