The Data Set of CNC machine tools field key core technology identification

[Objective] Combining textual content features and the complex network relationship between “science and technology”, this study conducts research on the identification method of key core technologies, aiming to provide intelligence support for governments, research institutions, and the industry to rationally formulate scientific and technological strategic plans, or carry out scientific and technological innovation activities. [Methods] The Sentence-BERTopic model is used to perform deep semantic fusion and knowledge topic clustering on sentence-level paper and patent text corpora. Based on the citation relationships of papers and patents, a “science-technology” knowledge topic complex network is constructed, and the traditional PageRank algorithm is improved by combining node quality characteristics, time decay factors, weights of incoming node edges, and outdegree, etc., to objectively rank the importance and influence of nodes in the field. Finally, key core technologies are selected in combination with the head/tail breaks method. [Results] An empirical study was conducted in the field of numerical control machines, resulting in the identification of 53 key core technologies, including thermal error modeling and compensation, numerical control machine tool control technology, and numerical control machine tool feed systems. When compared with relevant domestic and international policy plans, this outcome comprehensively encompassed the key core technologies within the domain, thereby demonstrating the scientific validity and rationality of the methodology employed.