Abstract: According to differences in texture based on health images and pest images, a method of determining"multi-texture features" of Zeuzera coffeae in Santalum album of northern counties in Hainan was put forward based on image diagnosis method of texture feature modeling. For each image type, 6 mathematical models were combined and evaluated by the extracted 4-dimensional multi-texture features. Results shows that model NO.1 which X axis was the mean entropy correlation mean and Y axis was the mean entropy energy mean was the best in both fitting degree and classification accuracy, and the classification accuracy reached 91.25%. Compared with stepwise clustering algorithm, K-means clustering algorithm and Logistic model two-classifying method, this method could reduce the computational complexity under the premise of ensuring classification accuracy, and provide reference for texture image classification.