Currently, most explainable multimodal fake news detection methods overlook the further research and utilization of explanation data and cross-modal features. As a result, while these explainable fake news detection methods provide explanations for model decisions, their detection performance does not surpass that of advanced multimodal detection methods. To address these issues, this study proposes an iterative explainable multimodal fake news detection framework. This method consists of a main model and an explanation module, both of which receive multimodal news as input. First, the explanation module uses the explanation data calculated by the DeepLIFT algorithm as one of the inputs to the main model, contributing to the decision-making process. Next, the main model calculates cross-modal relevant features and cross-modal supplementary features through a multi-task network framework. It refines the cross-modal supplementary features by re-weighting them with the coarse prediction scores from the cross-modal relevant features and combines multiple features to make the final model decision. Finally, the explanation module trains by transferring decision knowledge from the main model by using knowledge distillation. The main model and the explanation module are trained alternately, forming an iterative framework that enhances model detection performance while providing decision explanations. Extensive experiments on two publicly available fake news detection datasets demonstrate that the proposed method outperforms state-of-the-art multimodal fake news detection methods.