Cancer driver genes play a crucial role in the formation and progression of cancer. Accurate identification of cancer driver genes contributes to a deeper understanding of the mechanisms underlying cancer development and advances precision medicine. To address the heterogeneity and complexity challenges in the current field of cancer driver gene identification, this study presents the design and implementation of a cancer driver gene identification system, ACGAI, based on graph autoencoder and LightGBM. The system initially employs unsupervised learning with a graph autoencoder to grasp the complex topological structure of the biomolecular network. Subsequently, the generated embedding representations are concatenated with original gene features, forming gene-enhanced features input into LightGBM. After training, the system outputs predictive scores for each gene on the biomolecular network, achieving accurate identification of cancer driver genes. Finally, the system utilizes Web technology to create a user-friendly and highly interactive visualization interface, enabling cancer driver gene identification in the context of gene set analysis and providing biological interpretation for the identification results. Through rigorous testing, the system exhibits superior identification performance compared to other methods, demonstrating its effectiveness in identifying cancer driver genes.