AI Ideological and Political Education Evaluation Combining AHP and Multiple Heuristic Algorithms

An innovative approach to the advancement of contemporary education is the intelligent evaluation system. This study presents an intelligent evaluation technique to enhance the ideological and political education evaluation effect. First, the ideological and political education evaluation system is built using the hierarchical analysis approach. To efficiently evaluate political and ideological education, two new algorithms are introduced: the modified particle swarm optimization algorithm and the evolutionary algorithm. These algorithms optimize the feature weights of indicators. The research models can converge in the smallest amount of time in the price comparison matrix's convergence examination. The consistency tests of A1 and A2 matrices were 0.0241 and 0.0124 respectively, which were the best among similar models. The research model had the highest evaluation accuracy in the first and fifteenth classes, respectively, when comparing the comprehensive evaluation effect of ideological and political education, at 0.964 and 0.968. In addition, in the comprehensive performance test of the evaluation model, the evaluation time of the research model was within 1.2, and it was better than similar models in terms of evaluation time and relative error performance. The research technology will provide important technical support for the improvement of classroom education and teaching evaluation.