With the acceleration of business globalization, accurate translation of business English has become an important guarantee for cross-cultural communication. This paper proposes a language model-based context analysis method for business English translation, which aims to improve the quality and translation speed of machine translation. The study by integrating CNN module and improved Attention mechanism, the model not only improves the translation quality, but also significantly accelerates the training speed. Experimental results in the WMT14 Chinese-English translation task show that the proposed model outperforms the traditional model in terms of BLEU score, up to 23.45, which is far more than the comparison model. Meanwhile, the model shows a significant advantage in training speed, taking only 6 hours and 10 minutes for a BLEU score of 17.58, with a convergence time about two-thirds shorter than that of the traditional model. The experiments also show that the integration of the model’s attention mechanism and CNN module can effectively improve the translation quality and training efficiency, and the method can achieve higher translation quality in a shorter time. Taken together, this study can provide a more time-efficient and accurate solution in business English translation by optimizing the context analysis of machine translation.
