Accurate and reliable service quality prediction has become a key issue in service recommendation and network measurement scenarios. However, traditional methods for time-aware QoS prediction face two main challenges: (I) data sparsity makes it difficult to estimate and recover global information from the limited known data; (II) shallow learning models struggle to represent the intricate relationships between objects, and thus suffer poor prediction performance. To this end, we propose a time-aware QoS prediction framework that combines the merits of graph modeling, graph representation learning, and contrastive learning. First, a novel graph schema is proposed to capture the complex interactions between user-service-slots. Then, a prediction model is developed leveraging a graph convolutional network to learn the node representations by aggregating feature information from neighboring nodes. Finally, a novel contrastive learning strategy is used to improve the robustness of node representation. Experimental results on a large-scale dataset demonstrated that our proposed method significantly outperforms the state-of-the-art prediction methods on response time and throughput prediction tasks.