English translation technology based on knowledge graph and structure tree

Artificial intelligence has shown significant value in the fields of machine translation and cross language understanding, particularly in tasks such as polysemous word resolution, long sentence processing, and complex grammar parsing. To improve the semantic consistency and syntactic rationality of English translation, a translation model that integrates knowledge graph and structure tree is proposed. This model first implements semantic modeling of vocabulary entities and relationships through knowledge graphs, and then combines structural trees and nested translation strategies for hierarchical syntactic parsing and generation. The experimental results showed that the bilingual evaluation substitute index value of the proposed model reached 33.2, an increase of 2.4 compared to Transformer, the "translation editing distance" decreased to 48.6, a decrease of 3.6 compared to the baseline model, and the explicit ranking translation evaluation index improved to 33.8. On the test set, the bilingual evaluation substitute indicator remained above 34.1, and the "translation editing distance" decreased to 47.2. The research results indicate that the synergistic effect of knowledge graph and structural tree can effectively improve translation accuracy and fluency. The research provides a feasible path to solve the problems of semantic ambiguity and syntactic fragmentation in complex translation scenarios.