Research on Optimization of Machine Translation Algorithms for English Complex Long Sentences under the Deep Learning Framework

In the context of globalization, English is the dominant international language, which makes accurate translation of complex long sentences increasingly important. Traditional machine translation methods often struggle to handle long sentences with complex structures and nested semantics. Recently, deep learning frameworks, especially Transformer based models, have provided new opportunities for translating long sentences. However, issues such as ineffective capture of long-range dependencies and insufficient contextual semantic representation still pose challenges. To address these limitations, this paper proposes an enhanced deep learning framework that combines multi-scale attention mechanisms and dynamic semantic enhancement modules to significantly improve the translation quality of complex English long sentences. This study first examined the theoretical basis and limitations of the Transformer model in translating long sentences. Then, construct an innovative model architecture and validate it through experiments. The results indicate that the proposed method effectively alleviates the existing bottlenecks in long sentence translation, improves translation accuracy and coherence, and also enhances model generalization. This study provides novel theoretical insights and methodological advancements for the field of machine translation, offering valuable insights for the broader development of translation technology.