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2026 , Vol. 70 >Issue 2: 99 - 110

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.02.008

研究论文

科学知识向技术领域流动的关键影响因素挖掘研究

  • 王贤文 ,
  • 耿屿 ,
  • 田文灿 ,
  • 尹逸贤
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  • 1 大连理工大学公共管理学院WISE实验室 大连 116024;
    2 莱顿大学科学与技术研究中心(CWTS) 莱顿 2300AX;
    3 北京师范大学计算传播学研究中心 珠海 519085;
    4 北京师范大学新闻传播学院 北京 100875
王贤文，教授，博士，博士生导师，E-mail:xianwenwang@dlut.edu.cn;耿屿，博士研究生;田文灿，讲师，博士，硕士生导师;尹逸贤，博士研究生。

收稿日期: 2024-09-10

  修回日期: 2025-02-09

  网络出版日期: 2026-01-21

基金资助

本文系中央高校基本科研业务费“科学如何促进技术创新：影响因素及作用模式研究”（项目编号：DUT24RW303）研究成果之一。

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Research on the Key Influencing Factors of Scientific Knowledge Flow to the Technological Domain

  • Wang Xianwen ,
  • Geng Yu ,
  • Tian Wencan ,
  • Yin Yixian
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  • 1 WISE Laboratory, School of Public Administration, Dalian University of Technology, Dalian 116024;
    2 Center for Science and Technology Studies, Leiden University, Leiden 2300AX;
    3 Center for Computational Communication Research, Beijing Normal University, Zhuhai 519085;
    4 School of Journalism and Communication, Beijing Normal University, Beijing 100875
Wang Xianwen, Professor, PhD, Doctoral Supervisor, E-mail: xianwenwang@dlut.edu.cn;Geng Yu, Doctoral Candidate;Tian Wencan, Lecturer, PhD, Master’s Supervisor;Yin Yixian, Doctoral Candidate.

Received date: 2024-09-10

  Revised date: 2025-02-09

  Online published: 2026-01-21

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摘要

[目的/意义] 为明确科学知识向技术领域流动的具体机制，探索影响知识流动的关键因素，以期为提升科技成果转化效率提供理论支持。[方法/过程] 首先，基于知识流动理论，从知识主体和知识客体两个维度构建一个包含9类特征18个指标的影响因素体系；然后，以2011—2020年间生物化学与分子生物学领域发表的论文为样本，通过真实与虚拟的非专利引用关系构建知识流动模型；最后，应用XGBoost机器学习模型和SHAP（SHapley AdditiveexPlanations）可解释机器学习方法，对非专利引用中知识流动的影响机制进行量化分析。[结果/结论] 研究发现，论文权威性和知识关联性是推动知识转化的核心驱动因素。在知识主体方面，期刊权威性和作者权威性对科学知识向技术的转化具有显著影响；在知识客体方面，权威性知识与高匹配度的内容更易促进知识转化。归纳出5种主要的作用模式：对数型、指数型、U型、线性型和其他类型，揭示不同特征在知识转化过程中的动态影响。

关键词： 非专利引用; 知识流动; 关键影响因素; 可解释机器学习方法

本文引用格式

王贤文 , 耿屿 , 田文灿 , 尹逸贤 . 科学知识向技术领域流动的关键影响因素挖掘研究[J]. 图书情报工作, 2026 , 70(2) : 99 -110 . DOI: 10.13266/j.issn.0252-3116.2026.02.008

Abstract

[Purpose/Significance] To clarify the specific mechanisms of scientific knowledge flow to the technological domain and explore the key factors influencing this flow, can provide theoretical support for improving the efficiency of technology transfer. [Method/Process] First, based on the theory of knowledge flow, this paper constructed an influencing factor system with 9 categories and 18 indicators across three dimensions: knowledge actors, knowledge objects, and the environment. Then, using papers published in the field of biochemistry and molecular biology from 2011 to 2020 as a sample, a knowledge flow model was constructed through real and virtual non-patent citation relationships. Finally, the XGBoost machine learning model and SHAP interpretable machine learning method were applied to quantitatively analyze the influence mechanism of knowledge flow in non-patent citations. [Result/Conclusion] The study finds that knowledge relevance and paper authority are the core driving factors of knowledge transfer. In terms of knowledge subjects, journal authority and author authority have a significant impact on the transfer of scientific knowledge to technology. In terms of knowledge objects, highly relevant content and authoritative knowledge are more likely to facilitate knowledge transfer. This study identifies five major impact patterns: logarithmic, exponential, U-shaped, linear, and other types, revealing the dynamic effects of different features in the knowledge transfer process.

Key words： non-patent citation; knowledge flow; key influencing factors; interpretable machine learning method

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