图书情报工作

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2026 , Vol. 70 >Issue 2: 111 - 125

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.02.009

研究论文

基于专利知识模因网络的跨领域技术机会识别方法

  • 韦婷婷 ,
  • 张斯敏 ,
  • 吴茵茵
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  • 1 华南农业大学数学与信息学院 广州 510642;
    2 华南农业大学图书馆 广州 510642
韦婷婷，副教授，博士;张斯敏，硕士研究生;吴茵茵，副研究馆员，硕士，通信作者，E-mail:wuyinyin@scau.edu.cn。

收稿日期: 2024-12-20

  修回日期: 2025-02-17

  网络出版日期: 2026-01-21

基金资助

本文系国家自然科学基金项目“基于复杂专利知识网络链路预测的智慧农业技术研发机会识别研究”（项目编号：72404092）研究成果之一。

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Cross-Domain Technology Opportunity Identification Method Based on Patent Knowledge Meme Network

  • Wei Tingting ,
  • Zhang Simin ,
  • Wu Yinyin
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  • 1 School of Mathematics and Information, South China Agricultural University, Guangzhou 510642;
    2 Library, South China Agricultural University, Guangzhou 510642
Wei Tingting, Associate Professor, PhD;Zhang Simin, Master’s Candidate;Wu Yinyin, Associate Research Librarian, Master’s Degree, Corresponding Author, E-mail: wuyinyin@scau.edu.cn.

Received date: 2024-12-20

  Revised date: 2025-02-17

  Online published: 2026-01-21

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摘要

[目的/意义] 在当前科技迅猛发展的背景下，跨领域技术融合成为推动创新的关键动力。本文提出一种基于专利知识模因网络的跨领域技术机会识别方法，旨在从宏观到微观的视角，系统地挖掘和识别潜在的技术机会。[方法/过程] 首先，从宏观层面出发，构建基于共现—引用关系的专利网络，以识别不同技术领域间的关联结构，进而利用链路预测方法预测潜在的技术领域之间的关联。在此基础上，深入微观层面，从专利文本中抽取语义单元，识别关联领域下的知识模因。最后构建知识模因网络，全面捕捉技术领域间的显性联系和隐性关联，揭示深层次的知识流动模式。[结果/结论] 以智慧农业领域为例，实证研究结果表明，该方法能够准确识别出该领域内具有创新潜力的技术机会，为科研人员和企业提供了从宏观到微观的跨领域技术机会识别新途径，有助于促进知识的跨领域流动与整合，推动技术创新与产业升级。[目的/意义] 在当前科技迅猛发展的背景下，跨领域技术融合成为推动创新的关键动力。本文提出一种基于专利知识模因网络的跨领域技术机会识别方法，旨在从宏观到微观的视角，系统地挖掘和识别潜在的技术机会。[方法/过程] 首先，从宏观层面出发，构建基于共现—引用关系的专利网络，以识别不同技术领域间的关联结构，进而利用链路预测方法预测潜在的技术领域之间的关联。在此基础上，深入微观层面，从专利文本中抽取语义单元，识别关联领域下的知识模因。最后构建知识模因网络，全面捕捉技术领域间的显性联系和隐性关联，揭示深层次的知识流动模式。[结果/结论] 以智慧农业领域为例，实证研究结果表明，该方法能够准确识别出该领域内具有创新潜力的技术机会，为科研人员和企业提供了从宏观到微观的跨领域技术机会识别新途径，有助于促进知识的跨领域流动与整合，推动技术创新与产业升级。

关键词： 技术机会识别; 模因理论; 链路预测; 跨领域; 技术创新

本文引用格式

韦婷婷 , 张斯敏 , 吴茵茵 . 基于专利知识模因网络的跨领域技术机会识别方法[J]. 图书情报工作, 2026 , 70(2) : 111 -125 . DOI: 10.13266/j.issn.0252-3116.2026.02.009

Abstract

[Purpose/Significance] In the context of rapid technological development, cross-domain technology integration has become a key driver of innovation. This paper proposes a method for identifying cross-domain technological opportunities based on a patent knowledge meme network, aiming to systematically mine and identify potential technological opportunities from macro to micro perspectives. [Method/Process] Firstly, starting from a macro level, a patent network based on co-occurrence and citation relationships was constructed to identify the association structure between different technological fields, and then link prediction methods were used to predict potential associations between technological fields. On this basis, delving into the micro level, semantic units were extracted from patent texts to identify knowledge memes within related fields. Finally, a knowledge meme network was built to comprehensively capture both explicit and implicit connections between technological fields, revealing deeper knowledge flow patterns. [Result/Conclusion] Taking the field of smart agriculture as an example, empirical research results show that this method can accurately identify technological opportunities with innovative potential within the field, providing researchers and enterprises with a new approach to cross-domain technological opportunity identification from macro to micro levels. This contributes to promoting cross-domain knowledge flow and integration, driving technological innovation and industrial upgrading.

Key words： technology opportunity identification; meme theory; link prediction; cross-domain; technological innovation

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