Since the ineffectiveness of color features extracted by global and local feature extraction methods to describe the artistic style of painter, a new oil painting description method based on key region was proposed. Firstly, the information richness of oil painting region was defined by incorporating the proportion of primary color and color diversity to detect and select the key region of an oil painting. Secondly, the color features in 54 dimensions of the selected key region were used to describe the oil painting, those features were evaluated by Fisher Score, and the important features were selected to describe the key region of the oil painting, so as to highly reflect the painter artistic style. Finally, to verify the validity of the proposed method, the Naive Bayes classifier was used to realize oil painting classification. Two databases were established to perform simulation experiments. The database 1 includes 120 oil paintings by three painters, and the database 2 includes 513 oil paintings by nine painters from three different schools. The experimental results on database 1 show that, the accuracy of classification combined with Fisher Score is higher than the accuracy of ordinary classification, the accuracy of the proposed method for classifying oil paintings according to painter and school is 90% and 90.20% respectively. The experimental results on database 2 show that the accuracy of the proposed method for classifying oil paintings according to painter reaches 90%, which is 6.67 percentage points higher than that of Feature selected by Fisher Score ( Features-FS ) , and the accuracy of the proposed method for classifying oil paintings according to school is 90.20%, which is comparable to that of Features-FS. The features extracted by the proposed oil painting description method based on key region can effectively describe the artistic style of painter.