This study proposes a novel decentralized federated learning algorithm, DKFL, which combines knowledge distillation technology and has been successfully applied in the fields of image classification and demand forecasting in the industrial chain. This algorithm effectively improves the performance of the model through knowledge distillation, while retaining the privacy protection advantages of decentralized federated learning. In image classification tasks, our algorithm not only performs well on the CIFAR10 dataset, but also demonstrates significant improvement in classification accuracy on the automotive parts dataset in the automotive industry chain. In demand forecasting tasks, this algorithm was applied to the supply chain dataset of the industrial chain, achieving more accurate forecasting results. The experimental results show that the proposed method can effectively improve the generalization ability and prediction accuracy of the model while ensuring data privacy, providing new ideas and technical support for the promotion of federated learning in the application of the industry chain.