Multi-task Learning Framework for Intelligent Risk Assessment in Global Digital Cultural Trade

As global digital cultural trade expands, cross-border transactions face increasingly complex multidimensional risks, including policy compliance, cultural semantic adaptability, and transactional uncertainty. Traditional single-task risk assessment models struggle to capture these interrelated factors holistically. To address this gap, this study proposes an intelligent risk assessment framework based on multi-task learning that integrates heterogeneous data sources, including transactional records, policy documents, and cultural annotations. The model leverages a shared encoder and task-specific decoders to jointly predict three types of risks. Experiments conducted on a multimodal dataset collected from 2020 to 2024 demonstrate that the proposed framework outperforms single-task baselines across all tasks, with an average Macro-F1 improvement of approximately 12%. The cultural semantic risk task shows particularly strong performance in low-resource scenarios. These results confirm the effectiveness of the multi-task approach in enabling cross-task knowledge transfer and provide a scalable AI-driven solution for managing digital cultural trade risks in complex global environments.