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The purpose of this study is to build an efficient and accurate case early warning and risk assessment system through data mining technology. In the research, we adopted an ensemble learning algorithm, combining the advantages of deep learning and traditional machine learning, to conduct an in-depth analysis of a large number of historical case data in the field of education. The experimental results show that the early warning accuracy rate of the system on the test data set reaches 85%, which is 45 percentage points higher than that of the traditional method, effectively reducing the false report and false alarm rate of discipline inspection and supervision work, and significantly improving the work efficiency. 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