This article proposes a performance optimization method for English machine translation based on deep learning and introduces the Bahdanau attention mechanism. Using a parallel corpus of approximately 30,923 sentences for training and testing, the model achieved an average BLEU score of 45.83, showing excellent English translation results. Research results show that this method effectively improves translation accuracy, helps enhance global communication efficiency, and brings new development ideas to the field of language translation.