Research on AI-Driven Intelligent Services for Aging Database of Cultural Heritage

Han Muzhe; Chen Yihou; Liu Guifeng; Su Wencheng; Wang Zhengxing

doi:10.13266/j.issn.0252-3116.2026.15.003

Library and Information Service >

2026 , Vol. 70 >Issue 15: 33 - 48

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.15.003

RESEARCH PAPERS

Research on AI-Driven Intelligent Services for Aging Database of Cultural Heritage

Han Muzhe ,
Chen Yihou ,
Liu Guifeng ,
Su Wencheng ,
Wang Zhengxing

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1 Institute of Scientific and Technical Information, Jiangsu University, Zhenjiang 212013;
2 Library of Jiangsu University, Zhenjiang 212013;
3 Library of Jiangsu Ocean University, Lianyungang 222005

Han Muzhe, Lecturer, PhD, Master's Supervisor; Chen Yihou, Master's Candidate, Corresponding Author, E-mail:chenyihou2@163. com; Liu Guifeng, Professor, PhD, Master's Supervisor; Su Wencheng, Associate Research Librarian, PhD, Master's Supervisor; Wang Zhengxing, Professor.

Received date: 2025-05-15

Revised date: 2025-08-06

Online published: 2026-07-30

Supported by

This work is supported by the youth project of National Social Science Foundation of China titled “Research on Multimodal Knowledge Organization and Fusion of Unearthed Literature” (Grand No. 23CTQ038).

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Abstract

[Purpose/Significance] To address issues in cultural heritage aging databases, such as extensive resource organization and low service efficiency, this study guided by the theory of optimizing information transmission status, explores methods for knowledge recombination and reuse of cultural heritage aging databases in an AI context. It aims to provide an operable definition and implementation framework for intelligent services, thereby enhancing the protection and utilization efficiency of cultural heritage resources. [Methods/Process] This paper proposes a technical framework integrating the convergence of knowledge graphs and the generativity of large language models (LLMs) for complementary fusion. Based on GraphRAG and knowledge graphs, it puts forward ideas for the digital transformation of cultural heritage digital resources. Through steps including the semantic association construction for structured data, knowledge extraction from unstructured texts, and cross-modal resource hosting, it achieves data-driven reorganization of cultural heritage resources, constrains intelligent service outcomes to approach the ideal information transmission status. [Results/Conclusions] An empirical study on the Zhenjiang Jiaoshan stele database demonstrates the superiority of this approach in suppressing AI “hallucinations” and ensuring knowledge accuracy—validated through comparative analysis with generic large models from an information transmission status. Results confirm the feasibility of this solution for renovating aging cultural heritage databases, with the proposed theoretical framework and technical path hold further research and promotion value for intelligent services of cultural heritage resources.

Key words： Artificial Intelligence; cultural heritage; aging databases; Large Language Models (LLMs); GraphRAG

Cite this article

Han Muzhe , Chen Yihou , Liu Guifeng , Su Wencheng , Wang Zhengxing . Research on AI-Driven Intelligent Services for Aging Database of Cultural Heritage[J]. Library and Information Service, 2026 , 70(15) : 33 -48 . DOI: 10.13266/j.issn.0252-3116.2026.15.003

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