Social media highly facilitates people’s daily communication and disseminating information， but it is also a breeding ground for rumors. Therefore， how to automatically monitor rumor dissemination in the early stage is of great practical significance， but the existing detection methods fail to take full advantage of the semantic information of the microblog information propagation graph. To solve this problem， based on Heterogeneous graph Attention Network （HAN）， a rumor monitoring model was built， namely MicroBlog-HAN. In the model， a hierarchical attention mechanism including node-level attention and semantic-level attention was adopted. First， the neighbors of microblog nodes were combined by the node-level attention to generate two groups of node embeddings with specific semantics. After that， different semantics were fused by the semantic-level attention to obtain the final node embeddings of microblog， which were then treated as the classifier’s input to perform the binary classification task. In the end， the classification result of whether the input microblog is rumor or not was given. Experimental results on two real-world microblog rumor datasets convincingly prove that MicroBlog-HAN model can accurately identify microblog rumors with an accuracy over 87%.