Ideological and political theory teaching model based on artificial intelligence and improved machine learning algorithms

Abstract

In the era of artificial intelligence, traditional teaching models can be replaced by intelligent teaching models, thereby effectively improving the efficiency of ideological and political teaching. This paper proposes a multi-frame sliding window double-threshold clutter map CFAR algorithm and analyzes its detection probability and false alarm probability formula. Moreover, the ideological and political teaching system based on artificial intelligence and improved machine learning is designed based on the B/S model. In addition, this article analyzes the practical teaching performance of the model combined with actual teaching and analyzes the teaching effect of the model in ideological and political education. Through experimental research, it can be seen that the performance of the experimental group is significantly higher than that of the control group, which verifies that the algorithm constructed in this article has a certain practical effect.