大数据2025，Vol.11Issue(5)：101-116,16.DOI:10.11959/j.issn.2096-0271.2025061
基于图检索增强生成和少样本学习的美术作品鉴赏
Art appreciation based on graph retrieval augmented generation and few-shot learning
摘要
Abstract
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 content generated by the multimodal large language model.关键词
图检索增强生成/美术教育/多模态大语言模型Key words
graph retrieval augmented generation/art education/multimodal large language model分类
信息技术与安全科学引用本文复制引用
刘天扬,寇思佳,金旭,王文静,陆雪松..基于图检索增强生成和少样本学习的美术作品鉴赏[J].大数据,2025,11(5):101-116,16.基金项目
国家重点研发计划项目(No.2023YFC3341200) The National Key Research and Development Program of China(No.2023YFC3341200) （No.2023YFC3341200）
