The domain name plays an important role in cybercrimes. Existing malicious domain name detection methods are not only difficult to use with rich topology and attribute information but also require a large amount of label data, resulting in limited detection effects and high costs. To address this problem, this study proposes a malicious domain name detection method based on graph contrastive learning. The domain name and IP address are taken as two types of nodes in a heterogeneous graph, and the feature matrix of corresponding nodes is established according to their attributes. Three types of meta paths are constructed based on the inclusion relationship between domain names, the measure of similarity, and the correspondence between domain names and IP addresses. In the pre-training stage, the contrast learning model based on the asymmetric encoder is applied to avoid the damage to graph structure and semantics caused by graph data augmentation operation and reduce the demand for computing resources. By using the inductive graph neural network graph encoders HeteroSAGE and HeteroGAT, a node-centric mini-batch training strategy is adopted to explore the aggregation relationship between the target node and its neighbor nodes, which solves the problem of poor applicability of the transductive graph neural networks such as GCN in dynamic scenarios. The downstream classification detection task contrastively utilizes logistic regression and random forest algorithms. Experimental results on publicly available data sets show that detection performance is improved by two to six percentage points compared with that of related works.