基于“科学-技术”复杂网络的关键核心技术识别研究——以数控机床领域为例  

Identification of Key Core Technologies Based on“Science-Technology”Complex Network:Case Study of CNC Machine Tools

作　　者：曹琨 吴新年 白光祖 靳军宝 郑玉荣 李莉 Cao Kun;Wu Xinnian;Bai Guangzu;Jin Junbao;Zheng Yurong;Li Li(Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China;Key Laboratory of Knowledge Computing and Intelligent Decision,Gansu Province,Lanzhou 730000,China;Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;Chinese Academy of Engineering Innovation Strategy,Beijing 100088,China)

机构地区：[1]中国科学院西北生态环境资源研究院,兰州730000 [2]甘肃省知识计算与决策智能重点实验室,兰州730000 [3]中国科学院大学经济与管理学院信息资源管理系,北京100190 [4]中国工程科技创新战略研究院,北京100088

出　　处：《数据分析与知识发现》2025年第3期42-55,共14页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目(项目编号:20BTQ094);中国工程院重大咨询项目(项目编号:2023-HYZD-05);2021年度西部青年学者项目的研究成果之一。

摘　　要：【目的】结合“科学-技术”文本内容特征及复杂网络关系开展关键核心技术识别方法研究,为政府、科研机构和产业界合理制定科技战略规划、开展科技创新活动提供支撑。【方法】运用Sentence-BERTopic模型对句子级别的论文和专利文本语料进行深层次语义融合及知识主题建模,根据论文和专利文献的引文关系构建“科学-技术”知识主题复杂网络,然后结合节点质量特征、时间衰减因子、入链节点边的权重和出度等因素对传统的PageRank算法进行改进,并对领域内节点重要性和影响力进行排序,最后结合重尾分级法遴选出关键核心技术。【结果】在数控机床领域进行实证研究,从中遴选出热误差建模与补偿、数控机床控制技术、数控机床进给系统等53个关键核心技术,将此结果与国内外相关政策规划进行对比,基本涵盖了领域内重要的关键核心技术。【局限】缺乏对引用位置、引用动机、引用行为及句子目的等深入分析,可能影响识别的准确性。【结论】通过构建“科学-技术”复杂网络及KCR算法全面揭示科学和技术的知识结构及其拓扑特征,实现了关键核心技术的细粒度精准量化识别。[Objective]This study explores methods for identifying key core technologies by integrating the textual content characteristics of“science-technology”and complex network relationships.It supports governments,research institutions,and industries in formulating scientific and technological strategies and conducting innovation activities.[Methods]First,we employed the Sentence-BERTopic model to perform deep semantic fusion and knowledge topic clustering on sentence-level paper and patent text corpora.Then,we constructed a“science-technology”knowledge topic complex network based on the citation relationships of these documents.Third,we improved the traditional PageRank algorithm by incorporating node quality characteristics,time decay factors,the weights of incoming node edges,and outdegree.This approach ranked the importance and influence of nodes within the domain.Finally,we identified key core technologies using the head/tail break method.[Results]We conducted an empirical study on CNC machine tools and identified 53 key core technologies,including thermal error modeling and compensation,CNC machine tools control technology,and feed systems.A comparison with relevant domestic and international policy plans demonstrates that the identified technologies comprehensively encompass the key core technologies in the field.[Limitations]This study lacks an in-depth analysis of citation locations,motivations,behaviors,and purposes,which may affect identification accuracy.[Conclusions]This study reveals the knowledge structure and topological characteristics of science and technology by constructing a“science-technology”complex network and applying the Key Core Rank(KCR)algorithm.The proposed method achieves fine-grained and precise quantitative identification of key core technologies.

关 键 词：关键核心技术 技术识别 Sentence-BERTopic 复杂网络 重尾分级法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] G250[自动化与计算机技术—计算机科学与技术]