A new inductive microblog rumor detection method based on graph convolutional networks (GCN) was proposed to solve the problems faced by traditional GCN in rumor detection, such as the insufficient consideration of word semantic information and the difficulty of selecting pooling methods. Firstly, the semantic relationship between words was considered. A microblog event graph construction method based on word semantic correlation was proposed by combining the traditional word co-occurrence based graph construction method, and the node information aggregation was realized by combining GCN and gate recurrent unit (GRU). Then, in order to effectively fuse the feature information of different nodes, a multiple pooling methods fusion strategy based on attention mechanism, which fused max-pooling, average-pooling and global-pooling, was proposed to obtain the final graph level vector. Finally, in order to improve the efficiency of microblog rumor detection, the influence of microblog comment time on detection results was explored, and the best comment utilization time threshold for model training was obtained. Experimental results show that the performance of the proposed method is generally better than that of Text-CNN, Bi-GCN, TextING and other typical methods on the given datasets, verifying its effectiveness in the field of microblog rumor detection.