Cancer results from the accumulation of driver gene mutations. Therefore, identifying cancer driver genes is a key issue for the effective treatment and diagnosis of cancer. Previous studies have focused on integrating gene networks with multi-omics datasets and using graph neural networks (GNNs) to improve prediction performance. However, relying on GNNs is insufficient to fully integrate the biological information contained in multi-omics data and the interaction information within multi-gene relationship networks. In this paper, we propose a new method, called T3HGCN, which constructs a heterogeneous graph convolutional network (HGCN) from multi-view using six gene relationship networks, and introduces a test-time training framework for the HGCN to address the above challenges. T3HGCN consists of two self-supervised contrastive learning tasks, global contrastive learning and local contrastive learning, which can enable the model to effectively learn the global interaction information of each network and the biological information of each gene, respectively. The experimental results show that, compared to the existing methods, our model achieves better results on the area under the ROC curve (AUC) and the area under the precision - recall curve (AUPRC).