Architecture and Key Technologies of a Higher Mathematics Assistance Platform Based on Data Mining Technology

This paper proposes and designs a higher mathematics assistance platform based on data mining technology, primarily aiming to address the efficiency of resource scheduling in traditional teaching systems and further enhance personalized teaching capabilities. Regarding the platform's architecture, the data collection and preprocessing module is mainly responsible for gathering students' online learning behavior data and ensuring data accuracy through multi-level data cleaning and discretization processes. The core decision analysis module adopts an improved C4.5 decision tree algorithm, introducing the Fayyad boundary point judgment criterion, which significantly improves the calculation process of information gain and further reduces computational complexity. The platform's significant advantages over traditional systems are validated through experiments, providing data support for teachers. This paper systematically explores the platform's architecture design and key technologies. The decision-making data obtained through data mining methods enables accurate prediction of students' learning behaviors, showing broad application prospects.