摘要
在知识更新加速、信息爆炸的时代背景下,传统依赖借阅率、出版年限及人工经验的静态剔旧途径难以适应馆藏优化与知识服务需要,引发文献“过剩”和“失衡”情况同时出现。针对这个问题,该文提出构建基于大数据、知识图谱与人工智能的动态评估体系,实现文献使用、学术价值及知识关联方面的多维度监测与智能解析。在此基础上,研究探索了智能化剔旧预警系统、知识关联型评估模型及多维度决策协同机制的设计与应用,以推动馆藏管理从依靠经验转向依靠数据、从静态评估迈向智能预测。研究不仅为文献剔旧提供科学的决策佐证,增强资源利用率,还为智慧图书馆建设及知识生态优化提供技术支持,具有重要的理论及实践意义。
In the context of accelerated knowledge renewal and information explosion,traditional static disposal methods relying on borrowing rates,publication years,and manual experience have become inadequate for optimizing collections and meeting knowledge service demands.This has led to simultaneous occurrences of document"surplus"and"imbalance".To address this issue,the paper proposes constructing a dynamic evaluation system based on big data,knowledge graphs,and artificial intelligence,enabling multi-dimensional monitoring and intelligent analysis of document usage,academic value,and knowledge connectivity.Building on this foundation,the study explores the design and application of intelligent disposal early-warning systems,knowledge-association evaluation models,and multi-dimensional decision-making coordination mechanisms.This advancement facilitates the transition of collection management from experience-dependent to data-driven approaches,and from static evaluation to intelligent prediction.The research not only provides scientific decision-making support for document disposal and enhances resource utilization efficiency,but also offers technical support for smart library development and knowledge ecosystem optimization,demonstrating significant theoretical and practical significance.
作者
张昕
杨晓玮
林洪芳
ZHANG Xin;YANG Xiaowei;LIN Hongfang(Academy of Naval Submarines,Qingdao Shandong,266199,China)
出处
《文化创新比较研究》
2025年第35期110-114,共5页
Comparative Study of Cultural Innovation
关键词
数字技术
图书馆管理
文献剔旧
动态评估
智能化决策
知识图谱
馆藏优化
Digital technology
Library management
Document deaccessioning
Dynamic evaluation
Intelligent decision-making
Knowledge graph
Collection optimization
