<bold>TreeGPT</bold>：一种基于大语言模型的技术主题体系自动构建方法

doi:10.3772/j.issn.1000-0135.2026.05.004

情报学报

2026, Vol. 45

Issue (5): 663-677 DOI: 10.3772/j.issn.1000-0135.2026.05.004

情报理论与方法

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TreeGPT：一种基于大语言模型的技术主题体系自动构建方法

王浩峰, 高影繁, 王莉军, 姚长青

中国科学技术信息研究所，北京 100038

TreeGPT: An LLM-Based Automated Method for Constructing Technical Topic Architectures

Wang Haofeng, Gao Yingfan, Wang Lijun, Yao Changqing

Institute of Scientific and Technical Information of China, Beijing 100038

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摘要随着科技文献数量的持续增长，如何从大量非结构化文本中高效提取技术信息并构建具有层次化语义关系的技术主题体系成为技术情报分析领域的重要课题。本文提出一种基于大语言模型的技术主题体系自动构建方法TreeGPT，利用大语言模型的内在知识与语义向量化技术，完成对科技文献的主题识别与主题关系挖掘，最终得到目标领域的技术主题体系。为验证本文方法的有效性，以集成电路领域为例，与BERTopic（bidirectional encoder representations from transformers topic modeling）和HLDA（hierarchical latent Dirichlet allocation）两种方法进行对比与实证研究。研究结果表明，TreeGPT在语义准确性和层次清晰性上均显著优于传统方法，在大语言模型性能与成本开销之间取得了有效平衡，为领域知识的语义建模和技术情报分析提供了重要支持。

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	王浩峰
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	王莉军
	姚长青

关键词 ：主题建模, 层次化主题构建, 大语言模型, 技术主题识别, 科技文本

收稿日期: 2025-06-18

基金资助:新一代人工智能国家科技重大专项“面向复杂信息流的科技文献大模型增量构建”（2023ZD0121501）；中央级公益性科研院所基本科研业务项目“智能情报引擎关键技术研发”（ZD2026-09）。

作者简介: 王浩峰，2001年生，硕士研究生，主要研究领域为自然语言处理；高影繁，通信作者，1974年生，博士，研究员，主要研究领域为智能信息处理，E-mail：gaoyingf@istic.ac.cn；王莉军，1978年生，博士，副研究员，主要研究领域为数据挖掘；姚长青，1974年生，博士，研究员，主要研究领域为情报理论与方法；

引用本文:

王浩峰, 高影繁, 王莉军, 姚长青. TreeGPT：一种基于大语言模型的技术主题体系自动构建方法[J]. 情报学报, 2026, 45(5): 663-677.
Wang Haofeng, Gao Yingfan, Wang Lijun, Yao Changqing. TreeGPT: An LLM-Based Automated Method for Constructing Technical Topic Architectures. 情报学报, 2026, 45(5): 663-677.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.05.004 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I5/663

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