With the rapid development of technology, numerous service-oriented computing patterns have emerged, among which Blockchain as a Service (BaaS) has garnered widespread attention globally. As a crucial approach to blockchain application development, the proliferation of blockchain services has led to a large number of peers providing similar functionalities. Consequently, constructing a robust blockchain-based system necessitates carefully selecting dependable blockchain services (peers) that offer a superior quality of service (QoS). However, due to the vast array of available services and the limited availability of personalized QoS data, identifying the most suitable options poses significant challenges. Hence, we propose a novel graph neural network-based architecture, referred to as Double Attention (DBA). This framework integrates a multi-layer graph attention mechanism with self-attention to more effectively predict missing values in sparse scenarios. We conduct extensive experiments on a large-scale real-world dataset, and the results demonstrate that our DBA architecture not only achieves highly accurate predictions under sparse conditions but also outperforms existing approaches.