English Teaching Evaluation Combined with End-User Computing and Neural Network

Abstract

A neural network model and English teaching evaluation of university with end-user computing are the focus of this paper. The main research contributions are as follows: (1) Propose an ADA-BP neural network. It applies the adaptive learning rate as well as the momentum term to promote the BP network. Experiments show that the model solves the problems of the existing methods and models such as difficulty in determining weights, prone to overfitting, slow convergence, and prone to local minimums, which verifies the effectiveness in evaluating college English teaching. (2) Propose the DA-SVR network. The model has multiple hidden layers and performs multiple feature conversions to optimize the network. The output layer of the model uses SVR as a predictor to achieve evaluation. (3) Deploy the designed evaluation network on the server, and end users can use the API interface to directly access and test the model functions. Massive experiments have verified the effectiveness of the designed method.