Intent detection and slot filling are two important tasks for spoken language understanding. Considering the close relation between them, most existing methods joint them by sharing parameters or establishing explicit connection between them for potentially benefiting each other. However, most of them only consider single directional connection and ignore their cross-impact between them. Moreover, these joint methods treat the predicted labels as the gold labels, which may cause error propagation. In this paper, we propose a two-stage Graph Attention Interactive Refine (GAIR) framework. In stage one, the basic SLU model predicts the coarse intent and slots. In stage two, we select the top-k candidate labels from stage one and construct a graph to make full advantage of intent and slot filling information. By constructing such graph, our framework can establish a bidirectional connection between two tasks and refine the coarse result, which can better take full use of cross-impact between two tasks. Moreover, contextual regularization is introduced for better alleviating error propagation. Experiments on two datasets show that our model achieves the state-of-the-arts performance.