Research on big data women's portrait platform based on knowledge graph analysis

Qingshuang Dong; Bing Ding; Minghui Xia

doi:10.1117/12.3031221

19 July 2024 Research on big data women's portrait platform based on knowledge graph analysis

Qingshuang Dong, Bing Ding, Minghui Xia

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318175 (2024) https://doi.org/10.1117/12.3031221
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

This study adopts the scientific knowledge graph research methodology, utilizing Cite Space information visualization software and China National Knowledge Infrastructure (CNKI) for visual analysis. It focuses on 483 journal papers from the CNKI database about personalized courses based on women profiling in China, especially revolutionary women. The research visually analyzes the studies on personalized platforms based on women's portrait in China. The study reveals that establishing student profiles involves using data science and educational technology methodologies such as machine learning, data mining, and artificial intelligence to process and analyze student information. This investigation aims to assist educational professionals in swiftly and accurately detecting the developmental trends of platforms based on women's portrait from a vast literature corpus. Furthermore, it aids in tracking dynamic hotspots in personalized platforms based on women's portrait and understanding the developmental directions of such platforms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingshuang Dong, Bing Ding, and Minghui Xia "Research on big data women's portrait platform based on knowledge graph analysis", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318175 (19 July 2024); https://doi.org/10.1117/12.3031221

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