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语义与演化视角下的产业技术壁垒识别研究OACHSSCD

Research on Industrial Technology Barrier Identification from a Semantic and Evolutionary Perspective:The Case of Lithography

中文摘要英文摘要

产业技术壁垒的动态识别是破解核心技术封锁、构建安全可控产业链的战略基础.依托专利文本与管制清单,本研究提出基于语义与演化视角的产业技术壁垒识别方法.首先,融合 LDA 与 Word2Vec模型提取核心技术主题,提升主题识别精度;继而构建基于技术主题识别-技术竞争映射-技术管制关联的三级筛选机制,实现产业关键技术壁垒识别;最后,运用 DTM 模型刻画壁垒主题的时序演化并对未来趋势进行预测.以光刻机产业为实证对象,实证结果验证了所提方法的识别精度和稳健性,所获结果与国家战略部署高度一致,其主题演化轨迹始终与产业节点推进、区域性政策扶持等多维因素高度契合,具备显著的政策规划与研发布局参考价值.本研究突破传统静态分析局限,构建"特征识别-机理解析-趋势预判"的全周期研究范式,可为光刻机产业突破路径选择提供数据驱动的决策支持,其方法论框架可拓展应用于半导体装备、生物医药等战略领域的技术攻防研究.

Dynamic identification of industrial technology barriers is a strategic cornerstone for breaking the core technology blockades and building secure and controllable industrial chains.Relying on patent texts and control lists,this study proposes an industry technology barrier identification method based on semantic and evolutionary perspec-tives.First,the LDA and Word2Vec models are combined to extract core technical topics,enhancing the accuracy of topic recognition.Then,a three-level screening mechanism based on technology topic identification,technology com-petition mapping,technology control association is constructed to pinpoint key industrial technology barriers.Finally,the Dynamic Topic Model(DTM)is applied to depict the temporal evolution of barrier topics and predict future trends.Taking the field of lithography as a case study,empirical results validate the accuracy and robustness of the proposed method:the results are highly aligned with national strategic priorities,and its evolution trajectories strongly corre-spond with multi-dimensional factors such as industrial milestones,regional policy incentives,and has significant ref-erence value for policy planning and research layout.This study breaks through the limitations of traditional static anal-ysis by constructing a full-cycle research paradigm of"feature identification-mechanism analysis-trend prediction",providing data-driven decision support for overcoming technological bottlenecks in the lithography industry.The pro-posed methodological framework can be extended to strategic domains such as semiconductor equipment and biophar-maceuticals for technical competition and defense studies.

冉从敬;程凡;李旺;蒋云龙

武汉大学信息管理学院,武汉,430072武汉大学信息管理学院,武汉,430072武汉大学信息管理学院,武汉,430072武汉大学信息管理学院,武汉,430072

社会科学

产业技术壁垒动态主题模型(DTM)光刻机语义与演化演化趋势预测

Industrial technology barriersDynamic Topic Model(DTM)LithographySemantics and evolutionEvolution trend prediction

《信息资源管理学报》 2026 (2)

82-97,16

本文系国家自然科学基金面上项目"基于图卷积神经网络的新兴技术领域高质量专利识别及其演化研究"(72274084)山东省自然科学基金青年项目"基于专利计量与机器学习的校企技术合作供需智能匹配方法研究"(ZR2023QG105)的研究成果之一.(This work is supported by the National Natural Science Program of China,"Identification of High Quality Patents in Emerging Technologies Based on Graph Convolutional Neural Networks and Its Evolutionary Study"(72274084)and the Shandong Province Natural Science Foundation Youth Project"Research on Intelligent Matching Method of Supply and Demand for University-Enterprise Technology Cooperation Based on Patent Measurement and Machine Learning"(ZR2023QG105).)

10.13365/j.jirm.2026.02.082

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