Research on Innovation of Artificial Intelligence-Driven Translation and Communication Models

The rapid development of artificial intelligence has brought profound transformations to the field of translation and dissemination. Centered on AI-driven translation models, this study systematically examines the strengths and limitations of technologies such as neural machine translation and multimodal learning. The paper proposes a novel model integrating dynamic feedback and self-adaptive optimization. Through theoretical analysis and experimental validation, the model shows significant improvements in semantic understanding, domain-specific terminology adaptation, and multilingual translation performance. The research also highlights challenges like cultural transfer and support for low-resource languages, suggesting future advancements should focus on cross-modal integration and human-machine collaboration to drive translation toward greater intelligence and efficiency. This study offers valuable insights into translation technology innovation, contributing to effective cross-linguistic communication.