With the vigorous development of artificial intelligence technology, especially in the field of deep learning, English education and teaching models are facing unprecedented opportunities and challenges. In order to enable students to master English knowledge more efficiently and align with international standards, it is particularly important to study the optimization of English teaching models. This paper uses specific deep learning algorithms and techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model and optimize English teaching modes, aiming to solve many problems in English teaching and improve the quality of English teaching. Through deep learning algorithms, we can analyze students’ learning behavior, habits and grades, thereby providing them with personalized learning resources and teaching strategies. Deep learning technologies such as CNN and RNN are used to recognize keywords and phrases in text, as well as to process sequence data such as speech and text, helping teachers better understand students’ learning needs and interests, thereby adjusting teaching content and methods. In addition, the adaptive nature of deep learning algorithms allows automatic adjustment of teaching content and difficulty according to the actual situation of students, providing them with learning resources and support that better meet their learning needs. This study not only applies deep learning algorithms to optimize English teaching modes but also delves into how these algorithms affect the learning outcomes of students and the teaching efficiency of teachers. Through empirical research and case analysis, we hope to provide new ideas and methods for the future development of English education.