The graph neural network (GNN) has demonstrated strong performance in modelling graph-structured data across multiple application domains. However, existing GNN models do not fully exploit the information inherent in data. In particular, from labelled data, only labels are used as ground truth for supervision through loss functions, while the rich feature information embedded in labelled data is not fully explored. To address this limitation, we propose a prototype-guided attention mechanism for GNNs, a novel architecture that constructs class prototypes from labelled data and leverages them as task-relevant information to guide representation learning. By incorporating this information as input through an attention mechanism, the resulting node embeddings capture more comprehensive and accurate graph representations, which are provided for subsequent GNN layers. The proposed architecture can be integrated with various existing GNN models to enhance their learning capability, demonstrating wide applicability and flexibility. Experiments on node classification tasks across multiple benchmark datasets demonstrate that the proposed attention-based GNN architecture outperforms the corresponding GNN baselines in prediction performance, highlighting its effectiveness and potential for graph learning tasks.