Art appreciation based on graph retrieval augmented generation and few-shot learning

• Published in: 大数据 Vol. 11; pp. 101 - 116
• Main Authors: LIU Tianyang, KOU Sijia, JIN Xu, WANG Wenjing, LU Xuesong
• Format: Journal Article
• Language: Chinese
• Published: China InfoCom Media Group 01.09.2025
• Subjects: art education; multimodal large language model
• ISSN: 2096-0271

With the continuous advancement of quality education in our country, the influence of aesthetic education in subject education is becoming increasingly important. Appreciation of artworks is one of the important contents of aesthetic education, which can cultivate students' artistic ability and literacy. However, the lack of excellent art teachers and the imbalance of the development level of art education in various regions has led to many students being unable to receive high-quality art appreciation education. In this case, using multimodal large language models to tutor students in art appreciation has become a potential alternative. Using a multimodal large language model, this paper proposes a method based on graph retrieval augmented generation and few-shot learning to guide the model to generate art appreciation content that meets the needs of high school education. Experimental results show that compared with the comparison methods, this method can effectively improve the quality of the appreciation