As industrial chain data grows more complex, existing models struggle to capture temporal dependencies. Dynamic data is characterized by multi-sourcing and continuous evolution, while knowledge graphs enhance risk prediction by uncovering complex relationships. However, current methods treat static knowledge and temporal data separately, neglecting their interaction and failing to address evolving, long-term, and short-term patterns. To overcome this, we propose KGR-HATA, a Knowledge Graph Reasoning with Hierarchical Attention-based Temporal Aggregation method. It includes a subgraph mapping evolution module for dynamic interactions, a knowledge graph evolution module for temporal evolution, and a temporal aggregation module to capture both long- and short-term associations. Experimental results show that KGR-HATA outperforms existing methods in prediction performance.