Association Estimation of English Education Level Using Artificial Neural Network Algorithm

In order to improve the accuracy of English education level evaluation, this paper puts forward a design method of associated estimation model of English education level based on artificial neural network. Establish a multiattribute decision-making constraint parameter model for the correlation assessment of English education level, and analyze the multi-attribute decision-making and quantitative characteristics of the correlation assessment of English education level combined with the multi-dimensional explanatory variable and control variable parameter identification methods. Combined with the artificial neural network modeling method, the feature clustering analysis of the English education level is carried out; the adaptive learning and training method of the artificial neural network is used to establish the attribute fusion set and the semantic ontology feature distribution set of the multi-attribute decision-making for the correlation evaluation of the English education level; using the artificial neural network The network output layer fusion control method realizes the optimization of the multiattribute decision-making process. The simulation results show that the method has a good effect on the intelligent decisionmaking of the correlation evaluation of English education level, and improves the accuracy of the evaluation results of English education level.