Collaboration efficiency is of paramount importance for software development. Finding suitable developers is critical and challenging due to the difficulty of capturing developers’ expertises, relevance as well as the sparsity of explicit developer-task interactions. To tackle this problem, existing developer recommendation approaches focus on modelling the developer’s expertise or interactions with tasks based on their historical information. However, such approaches often suffer from low performance because they ignore some useful information that might help improve recommendation performance, such as 1) developers’ collaboration relationship; 2) the interaction relationship between developer and task; and 3) the tasks’ similarity relationship. To leverage the above-mentioned relationships, this paper proposes DevRec, a novel multi-relationship embedded approach for software developer recommendation. We first formally define the multi-relationships and then learn the vector representations (aka. embeddings) of developers and tasks. Second, we explicitly encoded the multi-relationships into the embedding process. Third, to refine the embeddings of developers and tasks, we recursively propagate the embeddings from their high-order connectivity based on graph convolution network. Moreover, to reveal the importance of different relationships, we generate their attentive weights based on attention mechanism. Finally, to evaluate the performance of DevRec, we conduct extensive experiments on two real-world datasets, and to investigate the usefulness of DevRec in practice, we conduct a user study at a large software company. The results show that DevRec outperforms other five state-of-the-art approaches significantly.