Comprehensive Evaluation Model of College English Teaching Quality Based on Improved Deep Recurrent Neural Network

The teaching level of college English teachers not only has a strong impact on the students' English learning but also determines the quality of English teaching. At present, many colleges are paying increasing attention to the evaluation of the teaching quality. The development of artificial intelligence has provided a way for colleges to conduct comprehensive scientific evaluation of the teaching quality. This study improves the deep neural network and proposes a new deep recurrent neural network algorithm. Furthermore, the improved sparrow search algorithm is introduced into deep recurrent neural network. Finally, a model is established to comprehensively evaluate college English teaching quality on the basis of the improved deep recurrent neural network. This model can not only fully extract and retain the valuable features in the data but also improve the evaluation performance. In addition, it can automatically optimize the number of neurons in the hidden layer of the model and eventually achieve accurate evaluation of college English teaching quality.