Stock Movement Prediction (SMP) is a challenging task that aims at predicting the future stock price trend of companies in the stock. Recent advances mainly apply the Graph Convolutional Network (GCN) to learn connections among companies for SMP. However, these methods usually ignore the semantics of the specific relations (e.g., investment and share) between two entities (i.e., companies and persons) on the market knowledge graph. Meanwhile, considering the long-chain cross-shareholding structures among entities, it is difficult for GCN to obtain high-order neighbor information over long distances. To address these two problems, we present an Attention-aware Multi-order Relation GCN for SMP (AMRGCN-SMP). Specifically, an attention-aware multi-channel aggregation manner achieves the weighted fusion of nodes across multiple semantic channels. Moreover, the dynamic update of the adjacent tensor can fuse the multi-order relation representations and bring more abundant long-chain connections. The experiments on the CSI100E and CSI300E datasets demonstrate that the proposed method achieves state-of-the-art performances compared with the recent advances.