Abstract: In response to the minimal inter-class differences in tongue images and the insufficient feature extraction by traditional networks， this paper constructs datasets for tongue image semantic segmentation and classification and conducts data preprocessing. Based on RepVGG network algorithm design and optimization， a multi-feature fusion tongue constitution classification network MTSNet based on convolutional neural network is proposed. MTSNet employs a multi-scale feature pyramid and combine high-level and low-level semantic information learned by the network to enhance the network’s representational capabilities. The addition of squeeze-excitation convolutional layers in the RepBlock module enables the network to focus more on information-rich features. The experimental results show that MTSNet significantly enhances classification performance across nine types of tongue constitutions， and its accuracy is 32.11 percentage points higher than that of AlexNet， 22.37 percentage points higher than that of SVM， and 17.68 percentage points higher than that of Resnet-18. Compared with the unoptimized RepVGG network， MTSNet achieves improvements of 9.90 percentage points in accuracy， 14.01 percentage points in macro-averaging， 9.90 percentage points in micro-averaging， and 11.09 percentage points in weighted-averaging. This tongue constitution， classification method provides scientific basis for users’ health management and has good reference application for traditional Chinese medicine’s adjunctive treatment and scientific research.

Key words:  , convolutional neural networks, tongue segmentation, feature fusion, tongue classification, traditional Chinese medicine constitution