Topology augmentation is a popular strategy to address the issue of over-smoothing in graph neural networks (GNNs). To prevent potential distortion of node representations, an essential principle is to enhance the separability between embeddings of nodes from different classes while preserving smoothness among nodes of the same class. However, differentiating between inter-class and intra-class edges becomes arduous when class labels are unavailable or the graph is partially labeled. While clustering offers an alternative for identifying closely connected groups of nodes, traditional clustering methods face challenges when applied to GNNs in terms of accuracy, efficiency, adaptability, and scalability to diverse graphs. To address these limitations, we introduce ClusterDrop, which uses learnable prototypes for efficient clustering and incorporates supervised signals to enhance accuracy and adaptability across different graphs. Experiments on six datasets with varying graph structures demonstrate its effectiveness in alleviating over-smoothing and enhancing GNN performance.