Abstract: The identification of traditional Chinese medicinal materials depends on the experience of Chinese pharmacists， with low efficiency and no unified quantitative criteria. Aiming at the fine granularity classification problem of Sichuan Ophiopogon japonicus， Liriope spicata and Zhejiang Ophiopogon japonicus， an improved MARNet-152（Multiscale-Attention Residual Network-152） model based on ResNet-152 neural network is proposed， which assists artificial identification of three easily-confused maidong decoction pieces automatically. An improved model， MARNet-152 is constructed based on ResNet-152 residual neural network， with group convolution of 3×3 convolutional kernels in the Bottleneck of the ResNet-152 network structure to extract and represent multi-scale features. The convolution attention mechanism module（CBAM） combining space and channel is introduced to make the model pay more attention to the recognition of target object details and have better interpretation. The classification accuracy of the improved network model reached 91.42% in the fine grained recognition of maidong image， which is 6.62 percentage points higher than that of the basic model， and could provide reference for the recognition of maidong image. The improved MARNet-152 model has higher generalization ability， and the recognition effect is significantly improved compared with the original ResNet-152 model.

Key words: Chinese medicine tablets identification, image classification, deep learning, residual networks, attention mechanism