Digital technology facilitates the transition of educational data from extensive collection to refined analysis, providing data support for teaching optimization and personalized instruction. Based on the multi-source and dynamic nature of educational process data, this paper identifies the core requirements and application challenges of refined data analysis, explores a data analysis methodology tailored to teaching scenarios, and proposes actionable approaches across three dimensions: data preprocessing, analytical model development, and practical result application. The aim is to unlock the inherent value of teaching data through scientific refined analysis methods, enabling precise diagnosis and efficient optimization of teaching processes, thereby offering methodological insights for digital education reform.