Deep Neural Network Adaptive Learning Model Design for English Literacy Instruction

Writing skills not only promote the learning of other English skills such as listening, speaking and reading, but also effectively promote the internalization of language knowledge, laying the foundation for further improving the development of students' comprehensive language skills.In this paper, with reference to the application path of information technology in English literacy teaching, we design a SCN-LSTM-based language model, and on this basis, we adopt a bidirectional recurrent network as the language model, and propose an improved SCN-BiLSTM network, which can effectively obtain the contextual relationship of the input sequence.Through the linear interpolation of the language model, the cached language model adaptation is obtained, and the teaching scene corpus is utilized to train the model, and the teaching context-oriented language model adaptation is obtained.Construct ANFIS model to improve the evaluation of English literacy teaching.After the empirical research experiment, the average English reading score of the students in the experimental class after the experiment is 53.631, which is 11.942 points higher than that before the experiment.The writing score is 8.45, which is 0.97 points higher than before the experiment.The application of the adaptive model of English reading and writing based on SCN-LSTM network is very effective.