Abstract syntax trees (ASTs) play a crucial role in source code representation. In addition to the syntactic structure of ASTs, the hierarchical relationships/structures among the nodes in ASTs are also essential to enhance the code representation. However, due to the large number of nodes in an AST and the typically deep AST hierarchy, it is still challenging to learn the hierarchical structures within an AST effectively. In this paper, we propose HELoC, a hierarchical contrastive learning model for source code representation. To effectively learn the AST hierarchy, we use contrastive learning to allow the network to autonomously predict the AST node level and learn the hierarchical relationships between nodes, which makes the representation vectors of nodes with greater differences in AST levels farther apart in the embedding space. By using such vectors, the structural similarities between code snippets can be measured more precisely. In the learning process, a novel GNN (called Residual Self-attention Graph Neural Network, RSGNN) is used, which enables HELoC to focus on embedding the local structure of an AST while capturing its overall structure. HELoC also learns the representation of AST paths and combines them with the AST hierarchy representation to obtain a comprehensive representation of source code. HELoC is self-supervised and can be applied to many source code related downstream tasks after pre-training. Our experimental results demonstrate that HELoC outperforms the state-of-the-art models when applied to code classification and code clone detection.