Document-level Event Argument Extraction (DEAE) is a highly challenging subtask of information extraction, especially in news scenarios where the event structure is complex. Most of the current methods are entity-based classification or generative frameworks, facing significant challenges when dealing with argument types that are not entities and handling complex event types. In this paper, we propose an iterative extraction framework for DEAE, which simulates human reading habits to iterate documents sentence by sentence. We utilize a long-term memory to effectively capture and utilize document context during iteration, compensating for the model’s limited global information. To assist the model in understanding the complex events during iteration, the extracted arguments, considered short-term information, are used to enrich a dynamic prompt for extraction. Experiments on the news dataset DocEE demonstrate that our model outperforms previous methods. The ablation study also proves the effectiveness of each module.