In the context of the globalization era, language conversion plays an important role in cross-cultural communication. Natural Language Processing (NLP) technology enables computers to automatically convert text from one language to another, i.e., to achieve cross-language communication and understanding through machine translation. In this paper, we first extract webpage content through distributed crawlers to construct a parallel corpus of cultural contexts in English and Chinese. We then conduct an empirical analysis of cross-cultural translation in English-Chinese bilingualism using the improved Transformer translation model. If you compare the PPL and HM values of the Transformer improved model to the baseline model, they are -54.18 and 24.79 points lower than the baseline model. This is better than the improved values of Transformer (+BD+DE), which were -38.41 and 15.63 points lower. This indicates that the Transformer improvement model outperforms other models in terms of language conversion quality, specifically the translation effect. Therefore, we have verified the effectiveness of NLP technology in enhancing the quality of cross-cultural language conversion.