Knowledge base question answering (KBQA) aims to provide answers to natural language questions from information in the knowledge base. Although many methods perform well when dealing with simple questions, there are still two challenges for complex questions: huge search space and information missing from the query graphs’ structure. To solve these problems, we propose a novel KBQA method based on a graph convolutional network and optimized search space. When generating the query graph, we rank the query graphs by both their semantic and structural similarities with the question. Then, we just use the top k for the next step. In this process, we specifically extract the structure information of the query graphs by a graph convolutional network while extracting semantic information by a pre-trained model. Thus, we can enhance the method’s ability to understand complex questions. We also introduce a constraint function to optimize the search space. Furthermore, we use the beam search algorithm to reduce the search space further. Experiments on the WebQuestionsSP dataset demonstrate that our method outperforms some baseline methods, showing that the structural information of the query graph has a significant impact on the KBQA task.