An Analysis of Research Trends on Data Mining in Chinese Academic Libraries

  • Liu HuanchengEmail author
  • Wu TingtingEmail author
  • Álvaro Rocha


With the advent of the era of big data, massive data make the informationization work of academic libraries severely challenging in all aspects. The application of data mining technology to the various services of academic libraries to cope with this challenge has gradually become the mainstream trend. In order to better detect the overall trends and research hot spots of data mining in Chinese academic libraries, 329 related studies were extracted from CNKI (China National Knowledge Infrastructure) and analyzed by using bibliometrics and keyword network analyses. Bibliometrics was used to analyze the distribution of the overall trends, authors, institutions, research levels, and keywords in the related field. Cluster analysis was applied to the keyword co-occurrence network to show hot issues in the application research of data mining in Chinese academic libraries. Results indicate that findings in this area generally show an upward trend, but few high-yielding authors are found, the volume of journals is low, research institutions are scattered, and the rate of funding support is not high. At the method level, association rules and cluster analysis are hot data mining technologies in Chinese academic library research. At the application level, the fields related to personalized services and big data continue to be hot research areas.


Data mining Bibliometrics Chinese academic library Keyword network 


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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Zhengzhou University of AeronauticsZhengzhouChina
  2. 2.Henan Collaborative Innovation Center for Aviation Economy DevelopmentZhengzhouChina
  3. 3.Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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