信息资源管理学报2025，Vol.15Issue(5)：82-98,17.DOI:10.13365/j.jirm.2025.05.082
基于政策-技术主题关联的技术机会识别与评估研究
Technology Opportunities Identification and Evaluation Based on Policy-Technol-ogy Topic Association:A Case Study of the New Energy Vehicle Industry
摘要
Abstract
Previous studies on technology opportunity identification are mostly limited to the perspective of technology itself,pay insufficient attention to policy documents that directly reflect the major strategic de-mands of a country.In view of this,this paper proposes a technology opportunity identification method based on policy-technology topic association,aiming to explore the potential gaps between policy demands and technological development.Specifically,this paper uses policy documents to represent policy demands and patent technologies to represent the degree of technological development.The identification of technology opportunities is divided into three modules:topic extraction of policies and technologies,topic association,and identification and evaluation of policy-technology integration opportunities.Firstly,this paper identifies policy and technology topics by constructing a LDA topic model.Then,word embedding and hierarchical cluste-ring methods are used to establish the mapping relationship between policy and technology.This paper locates technology opportunity by constructing identification quadrant according to the identification rules.Finally,This study takes the field of new energy vehicles as an example to verify the effectiveness of research framework,which pro-vides information support for country and research institutions to accelerate the layout in this field.关键词
技术机会识别/主题关联/政策分析/新能源汽车/文本挖掘Key words
Technical opportunity identification/Topic association/Policy analysis/New energy vehicle industry/Text mining引用本文复制引用
张子涵,李阳,吴柯烨..基于政策-技术主题关联的技术机会识别与评估研究[J].信息资源管理学报,2025,15(5):82-98,17.基金项目
本文系国家社科基金重大项目"前沿交叉领域识别与融合创新路径预测方法研究"(23&ZD225)研究成果之一.(This research is supported by the Major Project of the National Social Science Foundation"Research on Innova-tion Paths and Prediction Methods for Identification and Integration of Frontier Cross-Disciplinary Fields"(23&ZD225).) （23&ZD225）
