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Objective The objectification of tongue diagnosis is one of the important contents of modernization of Chinese medicine, and the quantitative description of tongue coating is its important indexes. The thickness of tongue coating in traditional classification is qualitatively divided into two categories:thick coating and thin coating, yet there is a lack of quantitative description. A method combining gray-level cooccurrence matrix (GLCM) and wavelet texture feature extraction is proposed in this paper. Methods Firstly, the orthogonal wavelet of Daubechies2 is adopted to decompose the tongue image into primary wavelet, and we calculate the average and variance of the three detail subgraphs as features. Secondly, GLCM is used to extract the contrast, inverse gap, energy and correlation of 0°, 45°, 90° the three directions in approximate subgraph. On this basis, classifier of support vector machine (SVM) is trained to analyze the tongue images qualitatively based on that features. At the same time, the support vector regression (SVR) model for quantitative analysis of tongue coating thickness is established. Results A total of200 cases of tongue samples are taken, of which 100 cases are thin fur and 100 cases are thick fur, 60 cases of thinfur and 60 cases of thick fur samples are selected to train the classifier, and 80 samples are test samples. Conclusions The experimental results show that compared with the traditional wavelet texture feature extraction method, the method proposed in this paper can improve the effect of classification of tongue coating thickness.