Accurate prediction of equipment remaining useful life (RUL) can optimize maintenance strategies, reduce costs, and improve overall efficiency. However, most existing methods rely on separately extracting temporal and spatial features, which hinders the effective fusion of temporal and spatial information. To address this issue, this paper proposes a dual-axis attention graph convolutional network based on multi-scale feature extraction for RUL prediction. The model first utilizes a cascaded scale-adaptive convolution module to perform multi-scale spatiotemporal feature extraction from raw sensor data, capturing spatiotemporal features across different dimensions. These features are then used to construct a spatiotemporal graph, where graph convolution operations are applied to uncover deep dependencies within the data. Finally, a dual-axis attention mechanism is designed to dynamically weight features along both the temporal and spatial dimensions, thereby enhancing the representation of critical features. In the experimental validation on the FD001 and FD004 subsets of the C-MAPSS dataset, the RMSE and Score were 11.87 and 236 for FD001, and 13.44 and 816 for FD004, respectively. The results show that this method has higher accuracy compared with other methods.