LAUSR

With the continuous fermentation of the thought of intelligence, artificial intelligence has extended its tentacles into the field of artistic creation and has begun to try intelligent creation. Painting creation based on artificial intelligence is called “intelligent painting.” For oil paintings, the computational language is a relatively complicated description. How to correctly identify the computational language of oil paintings is essential for establishing a large oil painting database. This paper constructs a meaningful learning similarity measure and multiclassification model based on the CCNN model to realize the classification of oil painting language. A cropped CNN model is used to extract language features, and on this basis, oil painting works are cross-compared and multiclassified. This method realizes the classification of oil painting language and the corresponding painter and achieves superior accuracy. This paper constructs a data classification method based on small samples, measures similarity through cross-comparison, and provides a measuring approach for classifying the language of oil paintings. The CCNN model proposed combines the best classification results of oil painting language, which improves the accuracy of oil painting language classification. Moreover, it further enriches the methods of oil painting language classification and image recognition under computational intelligence.