<p>Opensource communities utilize issues to promote knowledge sharing and discussions among developers. However, as the community scales, the number of issues increases dramatically. To ensure efficient circulation of issues and improve issues utilization, it is crucial to recommend appropriate issues to the developers. Nevertheless, developers’ participation in resolving issues varies depending on their levels of expertise, leading to long-tail and cold-start problems. Additionally, interactions between developers and issues exhibit diverse topological modalities, which presents a challenge for existing recommendation models which are often built on a single type of embedding space, leading to suboptimal performance. To capture complex topological information, we propose the cross-space topological contrastive learning for knowledge graph-aware issue recommendation method. It combines different sparse interaction signals from collaborative filtering and knowledge graph information in Euclidean space and hyperbolic space for dual-space information aggregation. By performing intra-space contrastive learning between multi-hop subgraphs within each space, the contribution of CF signals and KG information can be effectively balanced. Cross-space contrastive learning avoids the occurrence of representation shift in a single space. CTCK alleviates the noise generated during KG propagation and increases consistency between the representations in both spaces. Its effectiveness is further enhanced with our proprietary issue knowledge graph (ISSUEKG), which can be used as auxiliary information to alleviate the long-tail problem. Through extensive experiments on a real-world dataset, we demonstrate that CTCK significantly outperforms 12 state-of-the-art baselines, beating the best method by 4.74, 7.36, and 2.86% on average in terms of AUC, F1-score, and accuracy, respectively.</p>