—基于中国知网数据库资料

黄柯云^1，2  王旭^1，2，3

（1.中国政法大学证据科学教育部重点实验室，北京 100088；2.“2011计划”司法文明协同创新中心，北京 100088；3.中国政法大学法庭科学标准研究中心，北京 100088。）

【摘 要】目的 本文旨在探究 2005-2019 年间，中国知网数据库收录的法医学中文文献所反映的作者、机构合作情况，以及科研热点与前沿。方法 本文使用 CiteSpace.5.6.R2 工具的作者、机构合作网络分析功能，关键词共现网络分析功能。以聚类视图和时间区块视图的形式，结合频数、突现率等量化指标，探究关键信息的强度和关联。结果 作者合作网络呈现多聚类、密联系的结构；机构合作网络中，高频、高突现强度机构多为科研院校；关键词共现网络中，热点与前沿词汇多归属于法医临床学、法医病理学和法医遗传学领域，部分新兴科技词汇涌入法医学领域，如似然比、人工智能等。结论 在未来，进一步深化传统法医科研改革，加速发展新兴领域，通过数据化知识结构将传统知识领域互联互通。

【关键词】法医学；文献计量学；科研特征；知识图谱

【中图分类号】D915.13

【文献标识码】A

【文章编号】1674-1226（2020）03-0355-14

A preliminary study on bibliometrics of the development of forensic medicine in China from 2005 to 2019-Based on CNKI database. Huang Keyun^1，2 , Wang Xu^{1，2 ，3}. (1. Key Laboratory for Evidence Science, CUPL, Ministry of Education, Beijing,100088, China; 2. Center of Cooperative Innovation for Judicial Civilization, Beijing 100088, China; 3. Forensic Science Standards Research Center, CUPL.)

【Abstract】Objective: This paper aims to explore the cooperation of authors and institutions, as well as the hot spots and frontiers of scientific research reflected in the forensic literature collected by CNKI database from 2005 to 2019. Methods: The paper took full advantage of author and agency cooperative network analysis function and the keyword co-occurrence network analysis function of CiteSpace.5.6.R2 tool. Through clustering view and time block view, the intensity and correlation of key information are explored by combining quantitative indicators such as frequency and emergence rate. Results: The author cooperative network presents a structure of multi clustering and close connection. In the cooperation network, the high frequency and emergence intensity institutions are mostly scientific research institutions.In the keyword co-occurrence network, the hot and cuttingedge words mostly belong to the fields of clinical forensic science, forensic pathology and forensic genetics. Moreover, some emerging scientific and technological words flow into the field of forensic medicine, such as likelihood ratio, artificial intelligence and so on. Conclusion: In the future, we should further deepen the reform of traditional forensic scientific research, accelerate the development of emerging fields, and interconnect traditional knowledge fields through data-based knowledge structure.

【Key Words】Forensic medicine; Bibliometrics; Characteristics of research; Knowledge graph