The recognition of oral mucosal diseases mainly depends on doctors′ v isual observation and subjective judgment.This method leads to low accuracy of disease recognition and heavy wor kload of doctors.To solve the above problems,an oral mucosal disease recognition method based on multi-level feature fusion is proposed.There are two kinds of deep-level features and shallow features extracted from oral d isease images.The efficientNet model is used to extract the deep features.HSV,histogram of oriented gradiant (HOG) and gray level co-occurrence mat rix (GLCM) are used to extract the shallow features of color,shape and texture of oral diseases respectively.Afte r feature fusion,the random forest (RF) algorithm is used to select the features with greater feature importance,reducing the dimension of the feature.Finally, a variety of machine learning classifiers are combined for classification and re cognition.The datasets of oral mucoal diseases collected are used for experiment verification. The experimental results show that the method can achieve the accuracy (Acc) of 92.89%,sensitivity (Sen) of 89.91%, specificity (Spe) of 96.06% and area under the curve (AUC) of 98.09%.It effectively solves the problems of many misjudgments and low accuracy in recognition.