Legal Case Recommendation (LCR) is to find out the documents that are most similar to the input case from the judicial point of view. Since the legal documents are long texts and have strong legal attributes, the traditional recommendation method based on text similarity is difficult to accurately understand the legal documents, resulting in poor effect of LCR. To address this problem, we propose Recursive Graph Representation Learning (ReGRL) to hierarchically learn the information in the graph, and obtain a more informative graph representation to accurately understand the case. ReGRL captures nodes, edge, and community information at different levels to integrate information of different granularity. To achieve this, ReGRL performs top-down graph decomposition and bottom-up graph encoding in a recursive form, which allows ReGRL to flexibly control the depth of the learning layers and provide accurate case representations for LCR. Experimental results show that ReGRL can not only generate a good representation, but has a better performance compared to text representation methods for LCR. In addition, we also performed different experiments to analyze the principle of ReGRL and verify the effectiveness of recursive procedures.