Efficiency Optimization of Automatic English Translation Based on Multi-Interactive Feature Fusion NLP Algorithm

This study enhances the efficiency and accuracy of English automatic translation systems by integrating multiple interactive features with a natural language processing (NLP) algorithm. As globalization intensifies, the demand for effective cross-language communication increases, making translation system optimization crucial. Despite advancements in AI translation, challenges remain in efficiency, semantic understanding, and context mastery. This paper addresses issues such as efficiency lags, semantic misunderstandings, and context constraints, particularly when navigating cultural nuances—like the different meanings of "dragon" in Chinese and Western cultures— and idioms, such as the literal versus Italian translation of "break the ice." Traditional methods often struggle to convey the original intent accurately. Moreover, failures in context perception, especially in complex or implied contexts, lead to inaccuracies. To overcome these challenges, we propose an NLP algorithm that dynamically integrates syntactic, semantic, and pragmatic features, thereby improving translation accuracy and efficiency. This study offers valuable insights and approaches for optimizing translation systems and advancing natural language processing research.