Abstract Long-standing challenges in the identification of risks in green supply chain finance stem from the complexity of data structures and the intertwining of non-linear information, leading to uncertainty. This study aims to construct a two-stage deep learning model that integrates Generative Adversarial Networks with a Residual Autoencoder (GAN-SAE) and Deep Neural Networks with Multi-Kernel Support Vector Machines (DNN-SVM) to achieve the intelligent and precise identification of risks.