Author(s)

HongFu Zeng, ZhaoMin Liang^*, FanRui Wei, YiXun Lu

Affiliation(s)

College of Artificial Intelligence, Nanning University, Nanning 530007, Guangxi, China.

Corresponding Author

ZhaoMin Liang

ABSTRACT

With the increasing societal emphasis on educational equity in recent years and the continuous rise in the number of applicants for the National College Entrance Examination (NCEE), intelligent and accurate information-based reference platforms for college major selection and application processes have become crucial. Empirical evidence indicates that higher education program matching systems, which leverage complex data analysis of historical admissions data through an information mining architecture, have contributed to more scientific and rational resource allocation in educational institutions, while also enhancing fairness and improvement within the educational ecosystem. However, existing service solutions still exhibit shortcomings that need to be addressed. Concretely, this project adopts a Browser/Server (B/S) architecture during its design and development phases, utilizing technology that separates data presentation from business logic. The primary integrated development environments (IDEs) employed include mainstream tools such as IntelliJ IDEA, PyCharm, and Visual Studio Code (VSCode). The platform incorporates functional modules such as user authentication, student registration workflows, institutional information query operations, simulated application submissions, and an institution recommendation mechanism based on candidate behavior. This modular functional design inherently improves the overall user experience.

KEYWORDS

NCEE application preferences submission; Collaborative filtering algorithm; B/S architecture

CITE THIS PAPER

HongFu Zeng, ZhaoMin Liang, FanRui Wei, YiXun Lu. Design and implementation of an intelligent recommendation system for college entrance examination application preferences. Journal of Computer Science and Electrical Engineering. 2025, 7(5): 13-25. DOI: https://doi.org/10.61784/jcsee3074.

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