Design and Simulation of Multilingual Intelligent Language Service System for Cross-border Logistics Based on Multilingual Pre-training Modeling

In this paper, we propose a multilingual intelligent service system that integrates dynamic domain adaptation mechanism, constructs cross-language logistics knowledge graph (X-LogisKG), and realizes real-time translation, risk warning and path optimization functions in 12 languages. Design a hybrid multi-task learning framework to improve the accuracy of intent recognition to 87.3% in low-resource language scenarios. Constructed a logistics-language joint simulation model based on AnyLogic, and verified that the system can improve cross-border transportation time efficiency by 31.6% and reduce the error rate to below 2.4%.The results of this research demonstrate the excellent performance and good effect of the system, which supports the intelligent development of cross-border logistics.