Research on the Accuracy of Machine Translation in Cross-Cultural Communication Based on Embedded Neural Networks

The introduction of embedded neural network technology marks a significant leap forward in machine translation technology. This technology not only simulates the complex learning and understanding mechanisms of the human brain but also achieves precise capture and conversion of subtle differences and deep meanings in language through continuous algorithm optimization and iteration. This study mainly focuses on the accuracy of machine translation in cross-cultural communication using embedded neural network technology. Our aim is to explore in depth the potential of this advanced technology in overcoming language barriers, improving cross-cultural communication efficiency and quality. The study emphasizes the importance of deeply integrating machine translation technology with cross-cultural communication theory. Compared with traditional rule-based machine translation methods, embedded neural networks can better handle the complexity and diversity of language, reduce human set limitations and errors, and significantly improve translation accuracy. Through an interdisciplinary research perspective, the aim is to gain a deeper understanding of the unique habits, communication norms, and potential cultural differences in language use across different cultural backgrounds, in order to provide more accurate cultural context support for machine translation systems. This combination not only helps to improve the quality of translation, but also promotes mutual understanding and respect between cultures, contributing to the construction of a more harmonious and inclusive cross-cultural communication environment.