| Summary: | Modernism has brought a new fine art vision to the art of painting, making it show aesthetic characteristics that distinguish it from classical painting. In this paper, based on the method of information theory, the indexes of order, complexity, and saliency of images are proposed to characterize the digital features of different aspects of the visual art of painting. The FisherScore selection mechanism is used to evaluate and screen the excellent features extracted. The final features are input into the Key Area Description Network (KADN) to complete the digital construction of the features of painting art through the KADN algorithm, and finally, based on the Wikiart dataset, we carry out the experiments of feature extraction and analysis of the images of fine art paintings, and summarize the achievements of the development of the art of painting through the results of the experiments. The results show that the improvement of KADN’s classification performance for the style subset is reflected in the whole. For example, the class Ukiyo-e only has a Top-1 classification error rate of about 12.2% under the key region description network, while the Top-1 classification error rate of the Realism class reaches more than 61.5%. Modern painting art can be analyzed effectively using the feature extraction method, which is a powerful tool for exploring the evolution of painting features.
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