Abstract: The identification of cancer driver genes is an important research area in oncology research, holding significant value for a deeper understanding of the mechanisms of cancer development and achieving precision medicine. Currently, most research methods primarily rely on graph data to capture the characteristics of driver genes. However, the evolution process of cancer development involves many complex interactions among various biomolecules, making it difficult to fully reveal its essence using information from only single modality. Therefore, how to efficiently integrate multi-modal biological data to comprehensively identify driver genes has become a critical issue that needs to be addressed. To this end, this study proposes a cancer driver gene identification method, titled as TGMNN (text-graph multi-modal neural network), based on a combination of text and graph modality. This method builds upon the graph modality by further incorporating text semantic information related to genes and employs an interactive attention mechanism to achieve collaborative fusion of multi-modal data, thereby reducing the differences between modalities. Compared to existing methods, TGMNN can more effectively capture the information interactions between modalities, significantly improving the accuracy of driver gene predictions. Experimental results show that the AUC value of this method reaches 0.897, outperforming existing methods and high-lighting the model's effectiveness.