[Purpose/Significance] This article attempts to apply large language model technology to conduct research on knowledge discovery and exploration of red archival resources, and explores the application methods of generative artificial intelligence in the field of knowledge discovery of archival resources. [Method/Process] Taking Memorabilia of Party History Materials of the Southern Bureau as an example, by digitizing these red archival resources, this article established a metadata and vector database based on the chosen text and constructing a knowledge discovery framework for red archival resources utilizing a large language model. By combining retrieval-augmented generation and structured multi-turn question-answering applications with large language models, our research conducted tasks such as entity extraction and character trajectory extraction based on archival large language models, thereby reducing barriers to the visualization of archival resource data. [Result/Conclusion] This study presents a novel approach to the integration of generative artificial intelligence with red archival resources, offers a distinctive solution for the efficient, diverse, dynamic, and interactive development and display of red archival resources, and provides a reference for scholars in the field of archives and relevant departments.
Yang Jianliang
,
Wang Yiduo
,
Huang Meiwen
,
Basang Cangmujue
,
Zhang Yanqing
,
Liu Yuenan
. Knowledge Discovery and Exploration of Red Archive Resources Based on Large Language Model——Taking the Memorabilia of Party History Materials of the Southern Bureau as an Example[J]. Library and Information Service, 2025
, 69(15)
: 112
-123
.
DOI: 10.13266/j.issn.0252-3116.2025.15.010
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