Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Hangzhou, China

Research Article

Bibliometric Knowledge Mapping of Fishery Biology on Big Data

Download186 downloads
  • @INPROCEEDINGS{10.4108/eai.19-5-2023.2334288,
        author={Haifan  Che and Min  Ye and Jingwen  Zen},
        title={Bibliometric Knowledge Mapping of Fishery Biology on Big Data},
        proceedings={Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19--21, 2023, Hangzhou, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2023},
        month={7},
        keywords={knowledge mapping big data bibliometric analysis fishery biology},
        doi={10.4108/eai.19-5-2023.2334288}
    }
    
  • Haifan Che
    Min Ye
    Jingwen Zen
    Year: 2023
    Bibliometric Knowledge Mapping of Fishery Biology on Big Data
    ICBBEM
    EAI
    DOI: 10.4108/eai.19-5-2023.2334288
Haifan Che1,*, Min Ye2, Jingwen Zen2
  • 1: Beibu Gulf University, China
  • 2: Guangdong Polytechnic of Science and Trade
*Contact email: chehaifan@foxmail.com

Abstract

This paper reviews the research literature of fishery biology, summarizes the previous description of the connotation and characteristics of fishery biology, discusses the research of fishery biology in the development mode, evaluation system and evaluates the existing research results on this basis. The purpose of this research is to help researchers understand the key knowledge, evolutionary trends and research frontiers of current research by knowledge mapping. Using CiteSpace literature methodology, this study analyzed the data of the science network database, and found that: One is the development of the research field has experienced three stages, and some representative key scholars and key documents have been recognized; the other is the co-occurrence of citations and keywords in the public knowledge map of literature illustrates the hot issues in this aspect. The challenges posed by visualizing big data today are different. At the same time, the theoretical model, structure and structural dimension are still debated. This is a direction that researchers need to continue their studies in the future.