Innovative Research on the Quality Evaluation Method of English Translation Teaching in Colleges and Universities Based on Neural Network Modeling

Abstract

Abstract This paper presents BP neural network as an evaluation tool for English translation instruction in colleges and universities. It does this by analyzing and combing the structure of BP neural networks, combining the five first-level indices with the assessment of indexes data preprocessing method, and building an evaluation system based on BP neural networks. We get sample data for the teaching quality assessment indexes based on the example teaching, evaluate it, and confirm that the BP neural network is feasible to use in the evaluation model. Two randomly selected college English language teaching classrooms are used as an example for the class, and the teaching quality rating method is utilized and analyzed. The teaching quality evaluation system’s findings are used to investigate the influencing factors that affect the quality of English translation teaching in higher education institutions. The teaching effect is also discussed about the impact of course time and content arrangements. According to the analysis, the English translation teaching evaluation index system built around the BP neural network model has a substantial amount of credibility because Kendall’s harmony coefficient W = 0.756 χ 2 = 7.687 χ 2 (p − 0.025) = 7.249 indicates that the consistency credibility of the instructional quality evaluation system goes to the test at a significant level of P = 0.025 and the opinions are very uniform.