Feedback mechanism of English translation teaching based on deep learning

With the rapid development of artificial intelligence technology, particularly the successful application of deep learning in various fields, its potential in education has gradually emerged. This study focuses on exploring the feedback mechanism of English translation teaching based on deep learning, aiming to improve teaching quality and learning efficiency. By integrating experimental design, data collection, model construction and optimization, as well as data analysis and model evaluation, the research demonstrates that deep learning feedback mechanisms have significant advantages in enhancing students’ translation skills and improving the learning experience. Experimental results indicate that, compared with traditional instructional feedback methods, the deep learning-based approach performs better on key performance indicators such as accuracy, recall, precision, and F1 scores. Additionally, students expressed higher satisfaction with teaching feedback based on deep learning. This study not only confirms the application value of deep learning technology in education but also provides new perspectives and ideas for the future development of educational technology.