| Summary: | The emergence of Internet information technology has led to the development of MOOC-based online teaching methods. The study uses the traditional C4.5 algorithm for data mining to improve teaching quality and simplifies and quantifies it with the Taylor series and GINI index. The study also considers the uncertainty of data changes and the characteristics of MOOC teaching to design a parallel processing system of the HD-TG-C4.5 algorithm under the framework of the Hadoop platform. The experimental results show that the minimum data classification error of the algorithm is 2%, and the maximum recommendation accuracy of teaching resources is 92.6%. Moreover, the response time and resource search time of this algorithm system are significantly better than traditional algorithms in terms of system debugging. The average login response time is less than 0.87 s, and the success rate of system debugging reaches 90%. The probability value of students mastering teaching resource knowledge points is also above 0.7. The MOOC teaching system based on TG-C4.5 algorithm can effectively mine learner behavior data and reduce the complexity and consumption of C4.5 algorithm. The MOOC teaching system based on TG algorithm can provide technical support for the decision-making information of teaching participants and provide early warning information for predicting learning behavior.
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