Abstract: Aiming at taking the source Twitter texts as the research object, this paper deeply explores semantic information of Twitter body content and emphasizes structural features of heterogeneous rumor spreading social networks, so as to improve rumor detection effect. This paper combines one-hot encoding word embedding method and multi-head attention mechanism to extract primary semantic feature of source Twitter text content. Furthermore, the content-capsule module is constructed based on CapsNet to extract the deeper semantic features of text content, and the structure features of rumor propagation in social networks are extracted by combining with GCN-capsule module. In order to further enrich the input, two kinds of capsule vectors are fused with a dynamic routing mechanism. And then the classification results of source tweets are output,and source tweets rumors detection is finished. Experimental results show that the accuracy of the model proposed in this paper reaches 93.6%.