Real time feedback and E-learning intelligent entertainment experience in computer English communication based on deep learning

Traditional E -learning usually lacks real-time feedback and interactivity, which can not meet the needs of students' personalized learning. Computer English communication is an important aspect of cultivating students' language communication ability, which has a positive impact on students' learning results. In order to realize the real-time feedback of computer English communication and E -learning intelligent entertainment experience, this study stimulates students' learning interest and improves their learning effect by providing real-time feedback and personalized learning content. With the development of computer network technology, E -learning English platforms can provide students with intelligent entertainment experiences and real-time learning feedback. This article analyzes the real-time feedback and E -learning intelligent entertainment experience of computer English communication based on deep learning. Firstly, an in-depth analysis was conducted on the characteristics of English communication. English communication has specific grammatical structures and professional terms, which require quick and accurate understanding and expression of information. On this basis, a training dataset was constructed, preprocessed and annotated, cleaned to remove irrelevant information and noise, and then corresponding labels were added to each data sample. Subsequently, the constructed dataset was used to optimize the deep learning and recurrent neural network models, and the model parameters were optimized using a backpropagation algorithm. The experimental results show that the method designed in this article has higher accuracy and real-time performance, can timely identify learners' problems and provide personalized feedback, accurately evaluate learners' communication ability, and provide targeted feedback.