Article Details

Title · Authors · Abstract · Keywords · DOI · References

Development of Professional Diversion System Based on WeChat Mini Program

WANG Qingyue, XU Xiaojun

(Ningxia Institute of Science and Technology, Shizuishan 753000, China)

Section:Information Application · Vol. 2025, Issue 7 · Pages:157-160,167 · 2096-4706(2025)07-0157-05 · CLC number:TP311 · Document code:A · DOI:10.19850/j.cnki.2096-4706.2025.07.029

Abstract

In recent years, some colleges and universities gradually implement the professional category enrollment model. Students uniformly study basic courses in the early stage of the undergraduate stage. According to the requirements of the school, personal expertise and development direction, they choose specific majors in the major categories for diversion learning. However, the traditional manual way of professional diversion is inefficient and error-prone, making it difficult to meet actual needs. To this end, a professional diversion system based on WeChat mini program is developed. The system is developed by Eclipse and Weixin DevTools, the programming language is Java, the mainstream framework is SSM framework, and the MySQL database is used for data storage. The experimental results show that the system can help students quickly complete the volunteer filling and view the diversion results, and provide real-time volunteer information for teachers, which is convenient for teachers to flexibly adjust the volunteer allocation of students, and significantly improves the efficiency and accuracy of professional diversion work.

Keywords

category enrollment; professional diversion; mini program

References

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Funding

First class grassroots teaching organization for high-level undergraduate construction projects: Software Engineering Teaching and Research Office project funding from Ningxia Education Department in 2023

About the authors

Wang Qingyue (1978-), female, Han ethnicity, from Xiantao, Hubei, associate professor, master's degree, research direction: software engineering and machine learning.