An artificial intelligence method for comprehensive evaluation of preschool education quality.

Abstract

The evolution in the quality of teaching for preschool education is worth studying. In this article, we solved the qualitative problems in the comprehensive quality evaluation by suggesting a method of quantitative combination and establishing a set of indicators suitable for the comprehensive quality evaluation of students in the kindergarten. According to the experience summed up by previous scholars, the weight of each index is obtained by an analytic hierarchy process. This study analyzed the defects and causes of fuzzy comprehensive evaluation and the neural network model in the construction of early childhood and preschool education's comprehensive quality evaluation model and propose a Feedforward Neural Network (FNN) model. FNN combined with neural network (NN) and fuzzy logic characteristics introduces fuzzy concepts and fuzzy inference rules into neural networks of neurons, the connection power, and network learning. It improves the learning ability of NN and fuzzy evaluation of the power of expression and effectively exerts the advantages of fuzzy logic and neural network to make up for their shortcomings. However, the convergence speed is very slow. To solve this problem, the similarity measure was used to improve the number of hidden layer nodes of the network. The effectiveness and feasibility of the FNN improved hidden layer nodes are verified by an example so as to realize the automation of comprehensive quality evaluation.