Oil painting is a type of non realistic rendering technology that allows people to express the desired information and create artistic images through oil painting. Oil painting does not care about the authenticity of the images and has strong expressive and technical effects. The article introduced three elements of oil painting feature analysis, including color features, color harmony, and theme color extraction, and introduced image processing technology. By calculating the color feature vectors of oil painting features, the features of the oil painting were analyzed based on the feature data. Talking about the security of oil painting images disseminated in the network in the age of big data, the blockchain mechanism was introduced. Oil painting images were tested based on the average number of color bands and pixel ratios of oil painting features from different periods, as well as the changes in oil painting color features. An analysis was also conducted on the falsification of oil painting images under the blockchain mechanism. Data showed that the average number of color bands in early oil painting images was only 4.5. By the 19th century, the number of color bands had increased to 6.8, and the pixel ratio data had changed from 0.5 in the early stage to 0.28 in modern oil painting images. When the number of times oil painting images were shared online was between 100000 and 500000, the fraud rate was controlled within 10%, while under traditional network mechanisms, the highest fraud rate of oil painting images was 55%. The conclusion is that digital image technology can analyze the characteristics of oil paintings in different periods, and the blockchain mechanism can provide a secure network communication platform for oil painting image sharing, providing stronger security protection for oil painting image information.

Lailai Xie. Oil Painting Features Based on Big Data Blockchain Algorithm and Digital Image Technology. Frontiers in Art Research (2025), Vol. 7, Issue 2: 39-48. https://doi.org/10.25236/FAR.2025.070206.

[1] Tse, Nicole, Soriano M, Labrador A M. "Decision making, materiality and digitisation: Esteban Villanueva’s Basi Revolt Paintings of Ilocos." AICCM Bulletin 39.1 (2018): 42-54.

[2] Fan, ZhenBao, Kang Zhang, and Xianjun Zheng. "Evaluation and analysis of white space in Wu Guanzhong’s Chinese paintings." Leonardo 52.2 (2019): 111-116.

[3] Pasquale, Stefania, and Anna Maria Gueli. "Effect of water in color changes of historical paintings." Color Research & Application 46.6 (2021): 1265-1275.

[4] Chu, Wei-Ta, and Yi-Ling Wu. "Image style classification based on learnt deep correlation features." IEEE Transactions on Multimedia 20.9 (2018): 2491-2502.

[5] Li, Mao, Jiancheng Lv, and Chenwei Tang. "Aesthetic assessment of paintings based on visual balance." IET Image Processing 13.14 (2019): 2821-2828.

[6] Mao, Wenli. "Video analysis of intelligent teaching based on machine learning and virtual reality technology." Neural Computing and Applications 34.9 (2022): 6603-6614.

[7] Sarkowicz, Dominika, and Anna Klisińska-Kopacz. "Investigation of the painting idyll attributed to Henryk Siemiradzki: the unusual technology of a canvas painting executed on an enlarged photograph." Studies in Conservation 63.5 (2018): 251-266.

[8] Ma, Xiao, Beltran V, Ramer G. "Revealing the Distribution of Metal Carboxylates in Oil Paint from the Micro‐to Nanoscale." Angewandte Chemie 131.34 (2019): 11778-11782.

[9] Im, Chanjong, Yongho Kim, and Thomas Mandl. "Deep learning for historical books: classification of printing technology for digitized images." Multimedia Tools and Applications 81.4 (2022): 5867-5888.

[10] Mohie, Moustafa Attia.Ali N M, Issa A "A New Method of Lining Oil Paintings Using Polyurethane." Mediterranean Archaeology and Archaeometry 19.2 (2019): 9-21.

[11] Miliani, Costanza, Monico L, Melo M J. "Photochemistry of artists’ dyes and pigments: Towards better understanding and prevention of colour change in works of art." Angewandte Chemie International Edition 57.25 (2018): 7324-7334. 

[12] Castellano, Giovanna, and Gennaro Vessio. "Deep learning approaches to pattern extraction and recognition in paintings and drawings: An overview." Neural Computing and Applications 33.19 (2021): 12263-12282.

[13] Patel, Vishal. "A framework for secure and decentralized sharing of medical imaging data via blockchain consensus." Health informatics journal 25.4 (2019): 1398-1411.

[14] Bonaduce, Ilaria, Duce C, Lluveras-Tenorio A. "Conservation issues of modern oil paintings: a molecular model on paint curing." Accounts of chemical research 52.12 (2019): 3397-3406.

[15] Sultana, Maliha, Hossain A, Laila F. "Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology." BMC Medical Informatics and Decision Making 20.1 (2020): 1-10.