Intelligent Algorithm-Assisted Evaluation System Construction and Practice of Civic Education in Colleges and Universities

Abstract

Civic education is one of the basic contents of higher general education. The study uses a hierarchical analysis method, particle swarm optimisation algorithm and genetic algorithm to construct the evaluation system of civic education in colleges and universities, improves the GA-BP evaluation model through adaptive mutation genetic algorithm and entropy value method, and constructs EM-AGA-BP evaluation model of civic education in colleges and universities. The evaluation results and performance of this evaluation model in practice are analysed and tested. The fit of the EM-AGABP evaluation model of civic and political education in colleges and universities exceeds 0.90, and the evaluation index system of this paper is able to reflect the situation of civic and political education in colleges and universities. The highest score of Z-schools in the evaluation indexes of the first level of civic and political education is the construction of educational resources (3.81), and the lowest score is the construction of infrastructure (2.39). The highest-scoring second-level indicator is Civics Teachers (4.62), and the lowest-scoring is Facilities for Teachers and Students (1.88). The score interval on the third-level indicators is [1.30,4.93]. The absolute error distribution of the EM-AGA-BP evaluation model of college civic education is 0~0.07, which is significantly better than the absolute error distribution of the RBF neural network model (0~1.519). The absolute error of the predicted value of 85% of the evaluation results of the samples in the EM-AGA-BP model does not exceed 0.06. EM-AGA-BP is superior in the performance of the evaluation of ideological and political education in colleges and universities.