Abstract: Remaining Useful Life (RUL) prediction is a critical task in the health management of aircraft engines. To effectively model the complex temporal dynamics and spatial structural information, this paper proposes a novel RUL prediction method based on Multi-Scale Spatio-temporal Graphs with Fusion Attention (MSFA-STG). In the temporal dimension, we design parallel branches of short-term and long-term causal convolutions, which are fused using an attention mechanism to capture multi-scale temporal features. In the spatial dimension, both local graphs within subsystems and hyper graph structures across subsystems are constructed, and a gating mechanism is employed to adaptively fuse local and global spatial features. Furthermore, a cross-scale spatio-temporal attention module is introduced to enhance the modeling of critical spatio-temporal dependencies, thereby improving prediction accuracy. Experimental results on the C-MAPSS datasets demonstrate that the proposed model outperforms mainstream methods, showing strong generalization and high predictive accuracy.