Abstract

Abstract In today’s era of digital development of big data information, cross-cultural and cross-regional communication are becoming more and more frequent, and human beings have the right and desire to enjoy certain common cultural information, and it has been 38 years since China and Thailand established diplomatic relations in 1975. Cultural differences in Sino-Thai translation based on big data are studied in four aspects: inter-translation of personal names, inter-translation of geographical names, inter-translation of quantity sets, and inter-translation of date and time. This paper focuses on the research of cultural differences in Sino-Thai mutual translation based on big data analysis, constructing a bilingual word translation model with monolingual word vectors under the big data platform Spark, and conducting data analysis on the accuracy and efficiency of translation by collecting Chinese dataset and Thai dataset in parallelization algorithm in two structures SchemeA and SchemeB . The results show that the accuracy of the bilingual word vectors generated by either SchemeA or SchemeB improves as the size of the dataset increases. In addition, SchemeA is better than SchemeB in terms of accuracy and SchemeB is better than SchemeA in terms of efficiency performance, so Sche A me is suitable for translation tasks with high accuracy requirements, while Sche B me is suitable for translation tasks on large data sets. This study can improve the efficiency and accuracy of mutual translation between China and Thailand and train more translators. Today’s high technology brings much convenience to people, and social knowledge is constantly updated and developed. It is necessary to study in order to keep up with the changes of the times and to grasp the updates of the words of the times in order to translate well and more accurately, and this study not only contributes to the translation work in China but also has an important historical significance to the cultural exchange and communication between China and Thailand.

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