关键词类群分析法在学科领域研究热点识别中的应用初探——以作物育种领域为例

An Initial Exploration of Keyword Trait-Groups Analysis in Identifying Research Hotspots: A Case Study in the Field of Crop Breeding

[目的/意义]针对传统领域研究热点识别方法存在的低频词被掩盖、聚类主题词杂糅难以解释等问题,提出关键词类群分析法,通过构建符合领域研究范式的关键词类群模型,洞察科研细节和研究逻辑,实现领域研究热点的全面化、结构化揭示,为领域研究热点的高质量识别提供新思路。[方法/过程]提出关键词类群分析法概念,归纳不同学科领域科研论文摘要中的研究表达范式,阐述基于研究表达范式的关键词类群建立的原则和方式,构建利用该方法进行领域研究热点识别的模型。[结果/结论 ]实证分析选取2023年作物育种领域论文为数据集,验证方法的可行性和有效性。结果表明,相较于传统的研究热点识别方法,关键词类群分析法可以有效规避“孤立点式”的分析,从“中观层”的分析视角得到更丰富、精确的领域研究热点。该方法可为领域研究态势分析、领域知识创新决策等应用场景提供高质量的情报支撑。

[Purpose/Significance] Traditional methods in identifying research hotspots risk the obscuring of low-frequency terms and the tangled intermingling of clustered keywords, so the Keyword Trait-groups Analysis method(KTA) is proposed. By constructing models in alignment with research paradigms, KTA is adopted to discern detailed scientific insights and logical coherence, and reveal the comprehensive and structured revelations of research hotspots, which is useful for high-quality recognition within diverse academic fields. [Method/Process]After delineating the concept of KTA, it inducted the typical research paradigms within various academic disciplines. According to the principles and methodology of keyword trait-groups on the paradigms, it constructed a KTA model for pinpointing research hotspots. [Result/Conclusion] Papers in crop breeding in 2023 were selected as empirical dataset to validate the feasibility and effectiveness of KTA. The results demonstrate that, compared with the traditional method, KTA can effectively avoid isolated analysis, and enable an enriched dimensionality and more precise perspective from a meso level. This method can provide high-quality intelligence support for academic field trend analysis, research innovation strategies, and other applications.