数字图书馆论坛2025，Vol.21Issue(2)：1-11,11.DOI:10.3772/j.issn.1673-2286.2025.02.001
基于PaECTER-BERTopic与大模型的专利技术主题识别及演化分析
Patent Technology Topic Identification and Evolution Analysis Based on PaECTER-BERTopic and Large Model:A Case Study of Generative Artificial Intelligence
摘要
Abstract
To solve the current problems of poor vectorized representation of patent texts and insufficient interpretability of patent technology topic identification results,a method of patent technology topic identification and evolution analysis based on PaECTER patent pre-trained language model,BERTopic,and large model is proposed.Firstly,the PaECTER patent pre-trained language model is used to vectorize the patent texts.Secondly,the patent technology topics are identified based on the BERTopic model combined with KeyBERT,and the systematic analysis is carried out on the identified patent technology topics using the GPT-4o large model.Then,the similarity correlation calculation is performed on the patent technology topics based on PaECTER to generate the patent technology evolution path.Finally,taking the domain of generative artificial intelligence as an example,we verify the effectiveness of the proposed method.The experimental results show that compared with the traditional BERTopic model,the method proposed in this paper improves the interpretability,consistency,and diversity of patent technology topics,realizes the accurate identification of patent technology evolution path,and at the same time reveals the development status and evolution trend of technologies in the domain of generative artificial intelligence,which can provide theoretical reference for related research.关键词
专利文本/技术主题识别/技术演化分析/PaECTER-BERTopic/大模型Key words
Patent Text/Technology Topic Identification/Technology Evolution Analysis/PaECTER-BERTopic/Large Model引用本文复制引用
黄怡,隗玲,张凯..基于PaECTER-BERTopic与大模型的专利技术主题识别及演化分析[J].数字图书馆论坛,2025,21(2):1-11,11.基金项目
本研究得到国家自然科学基金青年科学基金项目"基于多视角科技知识图谱融合的新兴技术演化路径识别与预测方法研究"(编号:72304176)资助. （编号:72304176）
