Mining of Potential Fields and Structure Optimization of ESI Discipline in Universities Based on Artificial Intelligence Algorithm

Authors

  • Fang Zhao Southwest Jiaotong University Hope College, Chengdu 610400, Sichuan, China
  • Yuan Tu Jiangxi University of Technolog, Nanchang 213001, Jiangxi, China

Abstract

This study examines the application of artificial intelligence (AI) algorithms in optimizing discipline layout and identifying potential fields in higher education. The objective is to enhance the development potential and resource allocation efficiency of essential science indicators (ESI) disciplines in universities through technical means. The paper explores the theoretical foundations and ESI discipline evaluation criteria based on artificial intelligence technology, providing an in-depth analysis of these technologies’ specific applications in discipline layout optimization. By means of comprehensive data collection and preprocessing, this study establishes a framework for algorithm design and performance evaluation, enabling the precise identification and analysis of potential fields for discipline development. Furthermore, the practical utility of the algorithm in optimizing discipline layout strategies is examined and discussed, offering suggestions for algorithm improvement and future development directions. This work provides a scientific basis for optimal decision-making regarding courses layout in colleges and universities, supporting the optimal allocation of higher education resources and the effective formulation of discipline development strategies.

Keywords: Artificial Intelligence algorithm, ESI subject evaluation, Optimization of discipline layout, Potential field mining

Cite As

F. Zhao, Y. Tu, "Mining of Potential Fields and Structure Optimization of ESI Discipline in Universities Based on Artificial Intelligence Algorithm", Engineering Intelligent Systems, vol. 33 no. 4, pp. 445-455, 2025.




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Published

2025-07-01

Issue

Vol. 33 No. 4 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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