Application and optimization analysis of language translation function in cross-border E-commerce platforms

This paper introduces the development status and prospects of cross-border e-commerce platforms and the impact of language barriers on their service quality and user trust, and analyzes the needs and problems of cross-border e-commerce platforms in language translation. In this paper, an innovative deep learning-driven language translation solution is proposed to realize real-time multi-language and multimodal conversion by integrating neural machine translation, multimodal translation and domain-adaptive translation to improve the accuracy, fluency, adaptability, coverage, robustness, consistency, and evaluability of translation. This paper conducts experiments on several datasets and language pairs, compares with several other machine translation models, evaluates the translation quality and efficiency of this paper’s model on different language pairs and domains, and analyzes the strengths and limitations of this paper’s model when dealing with cross-border e-commerce related texts.