Research and Implementation of Education Resource Scheduling Algorithm Based on Machine Learning

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Nowadays, the digitalization of educational resources is developing rapidly, and reasonable and efficient scheduling of these resources has become the key to improve teaching quality and efficiency. The purpose of this study is to optimize the scheduling mechanism of educational resources through machine learning algorithm, especially in the real-time decision-making process of resource allocation and use. Therefore, we design and implement a data-driven educational resource scheduling framework, which integrates many machine learning models, including decision tree, support vector machine and neural network, to adapt to different types of data and needs. Through experimental analysis, these models can accurately predict resource demand and optimize resource allocation strategies, thus reducing waste and improving resource utilization efficiency.From the first quarter of 2023 to the fourth quarter of 2024, the cost savings increased from 200,000 yuan to 550,000 yuan, which shows that ML algorithm has played an increasingly important role in optimizing resource allocation and improving operational efficiency over time. The application of machine learning technology in educational resource scheduling in this paper can not only significantly improve the allocation efficiency of educational resources, but also help educational institutions to better understand the dynamic demand of resource use and provide scientific basis for educational administrative decision-making.