Using Machine Learning to Optimize Resource Allocation and Management Strategies in Colleges and Universities

This study aims to explore the application of machine learning technology in resource allocation and management in universities. As educational data grows and the complexity of higher education management increases, traditional resource management methods face many challenges. To this end, this paper uses a variety of machine learning models, including time series analysis, decision trees, and random forests, to optimize student enrollment prediction, teacher allocation, and teaching resource management. Through the analysis of real data, this study demonstrates the effectiveness of machine learning models in terms of prediction accuracy and resource optimization. In addition, the research also developed a university resource management decision support system, which can analyze and predict data in real time to assist managers in making more accurate decisions. The results of this study show that machine learning technology can significantly improve the efficiency and effectiveness of resource allocation in colleges and universities, and is of great significance in guiding the practice of higher education management.