Syntactic Transformation Optimization Algorithm for English-Chinese Translation Based on Dependency Parse Tree

In order to solve the problems of word order confusion and unnatural expression caused by word order differences in English-Chinese translation, this paper proposes a syntactic structure transformation optimization algorithm based on dependency syntactic analysis tree. This method extracts structural information from the source language through dependency syntactic analysis, and combines the rule-driven syntactic mapping mechanism with the deep structure optimization model to achieve more accurate structural transformation and word order reconstruction between English and Chinese. The system architecture includes dependency analysis module, syntactic mapping module and optimization generation module, which can output natural expressions that conform to Chinese syntactic habits while ensuring accurate semantic communication.In the experimental part, a test set was constructed based on the English writing data of college students, and the system was evaluated using multi-dimensional indicators such as BLEU, TER, dependency consistency and syntactic fluency. The experimental results show that the proposed method is superior to the traditional neural machine translation system in terms of translation accuracy and syntactic structure retention, and also significantly improves the naturalness of the generated content. This study not only promotes the development of structure-aware translation technology, but also provides a practical solution for translation optimization in educational scenarios, which has certain theoretical value and practical application potential.