Application of Decision Tree Classification ID3 Algorithm in Learning Strategy Recommendation of Online Teaching Platform

The role of strategy recommendation in the learning of online teaching platform is very important, but there is the problem that teaching resources are not comprehensive. The traditional education model cannot solve the problem of strategy recommendation in online teaching platform learning, and the recommendation is unreasonable. Therefore, this paper proposes a decision tree classification ID3 algorithm for interactive policy recommendation analysis. Firstly, information theory is used to analyze online teaching, and indicators are divided according to the requirements of strategy recommendation to reduce strategy recommendation in the interfering factor. Then, information theory recommends strategies to the online teaching platform, forms a strategy recommendation scheme, and recommends the results of the strategy Conduct a comprehensive analysis. MATLAB simulation shows that under the condition of certain evaluation criteria, the decision tree classification ID3 algorithm recommends the strategy of the online teaching platform The accuracy and strategy recommendation methods are superior to the traditional education model.