Construction and Optimization Strategies of Prediction Model for Undergraduate Major Settings in Private Universities from the Perspective of Artificial Intelligence
Year: 2025
Type: article
Abstract: With the popularization of higher education in China, private universities play a crucial role in strengthening the educational framework and promoting fairness. However, they encounter multiple difficulties in the setting of undergraduate majors. This paper takes artificial intelligence as the entry point and presents a prediction model for undergraduate major allocation that takes into account multiple factors such as strategies, consensus, market acceptance, and educational resources. The model is constructed based on data sample collection and processing, integrating strategy orientation, market trends, educational resources, and social recognition. The aim is to accurately predict the multi-dimensional needs of private universities. Research shows that this model can enhance the efficiency of major allocation decisions and reduce blind investment and resource waste. This paper further improves the model algorithm and plans optimization schemes from the dimensions of strategy, market, and resources, providing guidance for the professional planning of private universities. This research not only helps to improve the scientific nature of undergraduate major allocation in private universities but also points out a new direction for the deep application of artificial intelligence in the field of education.
Authors: Xiongfei Liu, Yu Ren, Qian Su, Lei Huang, Weimin Wu, Wenqiang Du, Tong Yin
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4410767190