Abstract
English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students' English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.
Highlights
With the improvement of China’s comprehensive strength, more and more people will choose to travel, work, and study, and more and more overseas people are willing to come to China for development. is promotes the economic exchanges between countries, and makes the cultures of different countries constantly collide and exchange. erefore, English teaching has been concerned by all walks of life
Simulation Results of English Teaching Quality Evaluation System Based on Optimized genetic algorithm (GA)-BP Algorithm
Experimental Results of English Teaching Quality Evaluation System Based on Optimized GA-BP Neural Network Algorithm
Summary
With the improvement of China’s comprehensive strength, more and more people will choose to travel, work, and study, and more and more overseas people are willing to come to China for development. is promotes the economic exchanges between countries, and makes the cultures of different countries constantly collide and exchange. erefore, English teaching has been concerned by all walks of life. Erefore, in the evaluation of English teaching quality, the nonlinear characteristics of BP neural network can build the corresponding model in the case of unclear data generation reasons and through the learning and training of sample data to achieve the desired effect and can effectively reduce the impact of human subjective factors on the final evaluation results, so as to get more comprehensive, reasonable, and scientific results [20, 21]. The entropy method is used to process the data, that is, to avoid the randomness and subjectivity of the data on the basis of the given index weight of the data, and the GABP neural network algorithm is used for learning and training, as shown in the following formula: x′pq xpq − sq x (18). Preprocessing data, using entropy value method to determine the weight of indicators, and calculate the initial evaluation results e optimal weight threshold is assigned to the neural network
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