图书情报工作

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2026 , Vol. 70 >Issue 7: 83 - 100

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.07.006

研究论文

从知识涌现到扩散：“新”—“兴”主题成长特征动态测度研究

  • 赵又霖 ,
  • 王月妹 ,
  • 王嘉杰 ,
  • 封丽 ,
  • 石燕青
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  • 1 河海大学商学院 南京 211100;
    2 南京大学信息管理学院 南京 210023;
    3 河海大学图书馆 南京 2111004 南京农业大学信息管理学院 南京 210095
赵又霖,副教授,博士,博士生导师,E-mail:sobzyl@hhu.edu.cn;王月妹,硕士研究生;王嘉杰,博士研究生;封丽,讲师,馆员,硕士;石燕青,副教授,博士,硕士生导师。

收稿日期: 2025-02-05

  修回日期: 2025-04-15

  网络出版日期: 2026-03-31

基金资助

本文系国家社会科学基金一般项目“社会感知驱动下用户画像时空数据知识组织研究”（项目编号：21BTQ055）研究成果之一。

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From Knowledge Emergence to Diffusion: Dynamic Measurement of “Novelty” to “Prosperity” in Emerging Topic Growth Characteristics

  • Zhao Youlin ,
  • Wang Yuemei ,
  • Wang Jiajie ,
  • Feng Li ,
  • Shi Yanqing
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  • 1 Business school of Hohai University, Nanjing 211100;
    2 School of Information Management, Nanjing University, Nanjing 210023;
    3 Hohai University Library, Nanjing 211100;
    4 College of Information Management, Nanjing Agricultural University, Nanjing 210095
Wu Ruohang, Doctoral Candidate; Mao Yihong, Professor, PhD, Doctoral Supervisor, Corresponding Author. E-mail: maoyh@njau.edu.cn.

Received date: 2025-02-05

  Revised date: 2025-04-15

  Online published: 2026-03-31

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on Spatio-Temporal Data Knowledge Organization of User Profiles Driven by Social Perception” (Grant No. 21BTQ055).

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摘要

[目的/意义] 在新兴主题成长过程中，主题涌现时的知识创新程度体现为“新”的特质，而主题扩散下的知识传播潜力则展现为“兴”的趋势。为解决当前测度方法中对主题创新来源探究不足以及跨领域扩散潜力动态反映缺失的问题，通过优化测度维度和指标框架，提升测度结果的准确度与时效性。[方法/过程] 从主题形成机制出发，贯穿知识涌现到扩散全貌，构建融合知识涌现创新性和知识扩散传播潜力维度的成长特征动态测度指标框架KEDT-GFDIM。在知识涌现创新性维度，基于主题关联网络分析新主题演化路径，结合时间、语义与路径结构差异计算主题创新性；在知识扩散传播潜力维度，基于主题扩散网络反映主题传播能力，通过引文关系引入IPC跨领域流动量化主题扩散潜力。[结果/结论] 在定量评估上，KEDT-GFDIM测度方法与传统测度方法相比，识别准确度提升了16.7%，且识别时效性得到提高，可更敏锐地检测到新兴主题或主题未来的发展趋势；在定性分析上，对动态成长曲线及国家相关政策进行分析可知，所识别出的三大新兴主题与其相一致，证明构建的动态测度方法能有效识别领域新兴主题。

关键词： 新兴主题; 知识涌现; 知识扩散; 复杂网络; 成长特征测度

本文引用格式

赵又霖 , 王月妹 , 王嘉杰 , 封丽 , 石燕青 . 从知识涌现到扩散：“新”—“兴”主题成长特征动态测度研究[J]. 图书情报工作, 2026 , 70(7) : 83 -100 . DOI: 10.13266/j.issn.0252-3116.2026.07.006

Abstract

[Purpose/Significance] In the growth of emerging topic, the knowledge innovation level during the emergence phase reflects the characteristic of “novelty”, while the knowledge diffusion potential in the diffusion phase shows the trend of “prosperity”. In order to solve the problems of insufficient exploration of the sources of topic innovation and the lack of dynamic reflection of cross-domain diffusion potential in the current measurement methodology, this paper aims to optimize the measurement dimensions and indicator framework to enhance the accuracy and timeliness of the measurement results. [Result/Conclusion] Starting from the topic formation mechanism, this study explored the whole process from knowledge emergence to diffusion, and constructed a dynamic measurement indicator framework, KEDT-GFDIM (Knowledge Emergence and Diffusion Trait Growth Dynamic Indicator Measurement Framework of Emerging Topics), integrating the dimensions of knowledge emergence innovation and knowledge diffusion potential. In the knowledge emergence innovation dimension, the evolution paths of new topics were analyzed based on topic association networks, and their innovation was calculated by considering temporal, semantic, and structural differences in the paths. In the knowledge diffusion potential dimension, the topic diffusion network was employed to reflect the dissemination capacity of emerging topics. Additionally, cross-domain IPC flow—derived from citation relationships—was introduced to quantitatively assess their diffusion potential. [Result/Conclusion] The results indicate that, in terms of quantitative evaluation, the KEDT-GFDIM measurement method outperforms traditional approaches, achieving a 16.7% improvement in identification accuracy and enhanced timeliness in detection. This enables more sensitive identification of emerging topics and potential future trends. From a qualitative perspective, analysis of the dynamic growth curves and relevant national policies demonstrates that the three identified emerging topics align well with policy directions, thereby validating the effectiveness of the proposed dynamic measurement method in detecting emerging topics within the domain.

Key words： emerging topics; knowledge emergence; knowledge diffusion; complex networks; growth feature measurement

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