Abstract
The purpose of this study is to explore the optimization method and application value of deep learning in automatic English translation system in cross-border trade. Through the comprehensive study of deep learning and automatic translation, the key role of deep learning in translation system is revealed. The study analyzes the needs and language barriers of English translation in cross-border trade and systematically evaluates different deep learning model optimization strategies. In this paper, the performance of the model is significantly improved through the comprehensive optimization of data preprocessing, model structure optimization, and performance evaluation. The experimental results show that the optimized model performs well on several performance indicators, which proves the great potential and practical application value of deep learning technology in the automatic translation system of cross-border trade English. This study provides scientific theoretical guidance and practical reference for English translation in cross-border trade practice, which is expected to help solve the language barrier in cross-border trade and promote the smooth and diversified development of international trade.
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