面向有组织产学研协同创新的合作主题挖掘  

Collaboration topic mining for organized university-industry-research synergy innovation

作　　者：黄璐[1,2] 任航 曹晓丽 陈翔 HUANG Lu;REN Hang;CAO Xiao-li;CHEN Xiang(School of Economics,Beijing Institute of Technology,Beijing 100081,China;Digital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology,Beijing 100081,China;National Science Library,Chinese Academy of Sciences,Beijing 100190,China;Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;School of Management,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学经济学院,北京100081 [2]数字经济与政策智能工业和信息化部重点实验室,北京100081 [3]中国科学院文献情报中心,北京100190 [4]中国科学院大学经济与管理学院信息资源管理系,北京100190 [5]北京理工大学管理学院,北京100081

出　　处：《科学学研究》2025年第3期548-559,共12页Studies in Science of Science

基　　金：国家自然科学基金面上项目(72274013,72371026)。

摘　　要：开展有组织的产学研协同创新,是发挥我国新型举国体制优势、实现产学研深度融合的重要内容。其中,对产学研“合作主题”的有效识别是实现高质量高效率产学研协同创新的首要问题。本文提出了一套基于复杂网络分析和深度学习算法的产学研协同创新合作主题挖掘方法。首先,围绕“有组织的产学研协同创新”概念和主题特征进行深度剖析,提出产学研合作主题应具有高价值性和强相关性两大特征;其次,基于论文数据和专利数据分别构建“科学主题词-学研机构”双层网络和“技术主题词-企业”双层网络,其中,SciBERT模型被用来构建科学和技术主题词语义网络,基于Node2Vec的链路预测模型被用来预测未来的科学和技术主题词语义网络;之后,应用复杂网络拓扑结构分析、社区发现、机器学习等方法对主题的新颖性、基础性、广泛性、成长性、前瞻性五大指标进行测度,识别高价值的科学主题和技术主题;最后,对语义相似度指标SimDoc进行改进,计算科学主题和技术主题之间的相关性,遴选产学研协同潜力大的“科学主题-技术主题对”。本文选取人工智能领域开展实证研究,对提出的研究方法进行验证。本研究能为国家、区域和行业组织高层级产学研协同创新提供重要的量化决策参考。Organized industry-university-research (IUR) collaboration is crucial for leveraging China's new national system advantages, enhancing the overall efficacy of the national innovation system, and achieving deep integration of industry, university, and research. Identifying collaboration topics is paramount for high-quality, efficient synergy in such collaborations and constitutes a primary challenge. This paper proposes a set of identification methods for IUR collaboration topic pairs based on complex network analysis and deep learning algorithms. Firstly, this paper analyzes the concept of "Organized IUR Synergy Innovation" and related topic features, proposes that the topic of IUR collaboration should be high-value and strong-relevance. Secondly, a bi-layer network of "science keywords-universities and research institutions" and a bi-layer network of "technology keywords-enterprises" are constructed based on paper data and patent data. The SciBERT model is used to construct a semantic network of science keywords and a semantic network of technology keywords. The Node2vec-based link prediction model is used to generate the future semantic network of science keywords and the semantic network of technology keywords. Then, the five indicators of novelty, fundamentality, width, growth, and foresight are measured using methods of complex network topology analysis, community discovery, and machine learning, to identify high-value scientific and technological topics. Finally, the correlation between scientific and technological topics is calculated using the improved semantic similarity index, SimDoc. This enables the selection of “science-technology topic pairs” that exhibit high potential for collaboration across IUR. In this paper, an empirical study is conducted using paper and patent data in the field of “artificial intelligence (AI)” from 2018 to 2022 to validate the research method and results. This study identifies 20 high-value scientific and technological topics in the field of AI. The results reveal t

关 键 词：产学研协同创新 产学研合作主题 SciBERT模型 复杂网络分析 有组织 

分 类 号：G353.12[文化科学—情报学]