Residual non-local attention graph learning neural networks are proposed for accelerating 4D-MRI. Stack-of-star GRE radial sequence with self-navigator is used to acquire the data. We explore non-local self-similarity features in 4d-MR images by using residual non-local attention methods, and we use a graph convolutional network with an adaptive number of neighbor nodes to explore graph edge features. A global residual connection of graph learning model is used to further improve the performance. Through exploring non-local prior, the proposed method has the potential to be used in clinical applications such as MRI-guided real-time surgery.