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<p>With the development of global tourism, language barriers have severely affected the travel experience of international tourists. This study presentss an automatic translation method for tourism English based on deep learning. In response to the demand for the automatic translation of tourism English, this study proposes a translation method based on deep learning. The background section highlights that with the development of tourism, language barriers have become an important factor affecting the experiences of international tourists. The research process involves an experimental design, including a baseline setting, evaluation index selection and experimental environment construction. Moses, Giza++, NMT, Google Translate, Microsoft Translator and other methods are used for comparison. The experimental results show that the method proposed in this study is significantly better than the others in terms of the BLEU and ROUGE scores and has a lower average delay time and higher throughput in real-time evaluation. The results indicate that this method effectively improves the quality and real-time performance of English tourism translations and has practical applied value.</p>

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The role of the tourism English automatic translation method based on deep learning in tourism economic development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月16日

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  • Xing Ming,
  • Dan Han

摘要

With the development of global tourism, language barriers have severely affected the travel experience of international tourists. This study presentss an automatic translation method for tourism English based on deep learning. In response to the demand for the automatic translation of tourism English, this study proposes a translation method based on deep learning. The background section highlights that with the development of tourism, language barriers have become an important factor affecting the experiences of international tourists. The research process involves an experimental design, including a baseline setting, evaluation index selection and experimental environment construction. Moses, Giza++, NMT, Google Translate, Microsoft Translator and other methods are used for comparison. The experimental results show that the method proposed in this study is significantly better than the others in terms of the BLEU and ROUGE scores and has a lower average delay time and higher throughput in real-time evaluation. The results indicate that this method effectively improves the quality and real-time performance of English tourism translations and has practical applied value.