Research on Computer Intelligent Comprehensive Evaluation Model of Education Using Neural Network and Principal Component Analysis

The prosperity of a country is always accompanied by the prosperity of education, and higher education is a very important part of the civic education system. Therefore, the paper set up the evaluation and prediction model of higher education, and apply it to six countries——United States, Germany, Japan, Australia, South Africa and India, to put forward reasonable measures and analyze the impact. In order to evaluate the health status of higher education system, the paper established CIPP health evaluation model based on PSO-BP neural network. The paper use 18 indicators and the relationship betthe paperen health status and related indicators to establish a clear framework of “foundation input process performance”. The index set is divided into four dimensions, according to the principal component analysis method to determine the extraction of four primary indicators. Through particle swarm optimization algorithm to optimize the neural network, the paper use a PSO-BP neural network algorithm to find out the health status of higher education. Our model is applied to six countries. The results show that the higher education health scores of the United States, Germany, Japan, Australia, South Africa and India are 4.95, 3.10, 2.88, 3.12, 1.38 and 1.78 respectively. According to this, it is concluded that the higher education health of the United States is excellent, while that of South Africa and India should be improved.