Code search is an important research topic in natural language processing and software engineering. Developing efficient code search algorithms can significantly improve the code reuse and the working efficiency of software developers. The task of code search is to retrieve code fragments that meet the requirements from the massive code repository by taking the natural language describing the function of the code fragments as input. Although the sequence model-based code search method, namely DeepCS has achieved promising results, it cannot capture the deep semantics of the code. GraphSearchNet, a code search method based on graph embedding, can alleviate this problem, but it does not perform fine-grained matching on codes and texts and ignores the global relationship between code graphs and text graphs. To address the above limitations, this study proposes a code search method based on a relational graph convolutional network, which encodes the constructed text graphs and code graphs, performs fine-grained matching on text query and code fragments at the node level, and applies neural tensor networks to capture their global relationship. Experimental results on two public datasets show that the proposed method achieves higher search accuracy than state-of-the-art baseline models, namely DeepCS and GraphSearchNet.