Research on university information management based on graph neural network

DOI

10.2991/978-94-6463-264-4_47How to use a DOI?

Keywords

University information management; Graph neural network; Relation extraction

Abstract

University information management plays a vital role in the current digital age, which covers many aspects such as student information, educational administration, scientific research results and so on. However, due to the large amount of information in colleges and universities, the traditional information management methods have been unable to meet the growing needs. Therefore, this paper proposes a university information management method based on graph neural network.First, we introduce the basic concepts and principles of graph neural networks. Graph neural network is a kind of machine learning model that can learn and represent graph structure data, it can capture the complex relationship between nodes and the global structure of the graph. This makes graph neural network an ideal tool to deal with information management problems in universities.Finally, we evaluate the performance of the university information management method based on graph neural network. We use the actual university information data set to carry out the experiment and compare with the traditional information management methods. The experimental results show that the method based on graph neural network has achieved remarkable improvement in the aspects of student information query, educational administration management and scientific research results analysis. It can more accurately capture relationships between students, uncover hidden patterns, and provide more accurate predictions and decision support.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Caiping Liang
AU  - Wumin Huang
PY  - 2023
DA  - 2023/09/28
TI  - Research on university information management based on graph neural network
BT  - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
PB  - Atlantis Press
SP  - 420
EP  - 426
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-264-4_47
DO  - 10.2991/978-94-6463-264-4_47
ID  - Liang2023
ER  -