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In the context of recent advances in Graph Convolutional Networks (GCNs) for semi-supervised learning, a significant highlight is the potential of Graph Contrastive Learning (GCL). Many established GCL-based GCNs focus on developing various augmentation strategies and learning architectures to efficiently generate and learn self-supervised information. Unfortunately, the node representation fusion is often neglected in the process of learning representation. To address the issue, we propose a novel end-to-end GCL framework called Perturbation-augmented Graph Convolutional Networks (PA-GCN), by simultaneously considering supervised information and self-supervised information of graphs to fuse node representations from the input graph and the augmentation graphs. Essentially, PA-GCN develops a perturbation augmentation strategy based on original graph relations, serving as a basis to generate diverse node information and self-supervised information. Additionally, PA-GCN introduces three main components: A encoder for node representation learning; an attention mechanism for effective node representation fusion; and a discriminator capable of learning self-supervised information from unlabeled nodes, thereby enabling joint learning of supervised information, node representation fusion, and self-supervised information. We conduct experiments on five real world datasets to evaluate the performance of PA-GCN in semi-supervised node classification tasks. The experimental results demonstrate the strong adaptability of PA-GCN for base encoders and the superiority of PA-GCN over the most advanced methods. [ABSTRACT FROM AUTHOR]