Research on the Construction of Machine Translation Model of Fuzzy Language in Ancient Chinese Medicine Books for the Transmission of Chinese Medicine Culture

In order to improve the translation quality of fuzzy language in ancient Chinese medical texts, a hybrid translation model integrating deep learning and knowledge graph is constructed to perform context modeling and disambiguation optimization for the literary syntax, semantic ambiguities, and terminology of the ancient texts. The Bi-LSTM context modeling module is designed to enhance semantic understanding with knowledge graph, and a multi-strategy fusion translation decision mechanism is used to improve translation accuracy. The results show that the model performs well in the processing of ambiguities such as disease dynamics and treatment principles, improves terminological consistency and semantic retention, and provides effective support for cross-lingual communication of TCM culture.