Bibliometric analysis of support vector machines research trend: a case study in China

  • Dejian Yu
  • Zeshui XuEmail author
  • Xizhao Wang
Original Article


Support vector machine (SVM) is a widely used algorithm in the field of machine learning, and it is a research hotspot in the field of data mining. In order to fully understand the historical progress and current situation of SVM researches, as well as its future development trend in China, this paper conducts a comprehensive bibliometric study based on the publications from web of science database by Chinese scholars in this field. First, this paper focuses on some of the basic characteristics of the research publications of SVM in China, including important journals, research institutions and countries/regions, most cited publications, and so on. Then, based on the knowledge mapping software VOSviewer, the cooperation between other countries and China as well as the cooperation between research institutions in China are explored. Finally, VOSviewer based bibliometric visualization graphics are used to identify the changes of the research hotspots in the SVM field. This paper provides a relatively broad perspective for the evaluation of SVM scientific researches, and reveals the development trend in this field.


Bibliometric analysis Support vector machines Co-citation Co-occurrence China 



This manuscript was supported by the Ministry of Education of Humanities and Social Science project (No. 19YJC630208), the Qinglan Project of Jiangsu Province (2019), the National Natural Science Foundation of China (Nos. 71771155, 71571123), and the Natural Science Research Project of Jiangsu Higher Education Institutions (19KJB120008).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Business SchoolNanjing Audit UniversityNanjingChina
  2. 2.Business SchoolSichuan UniversityChengduChina
  3. 3.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

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