The recognition of oral mucosal diseases mainly depends on doctors′ visual observation and subjective judgment.This method leads to low accuracy of disease recognition and heavy workload 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 disease images.The efficientNet model is used to extract the deep features.HSV,histogram of oriented gradiant (HOG) and gray level co-occurrence matrix (GLCM) are used to extract the shallow features of color,shape and texture of oral diseases respectively.After 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 recognition.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.