For document-level event argument extraction, existing role-based span selection strategies suffer from several limitations: (1) ignoring interrelations among arguments within an event instance; (2) relying on pre-trained language models to capture role semantics at either the event pattern or document, without leveraging pattern-instance associations. To address these limitations, this paper proposes a multi-round role representation learning strategy. First, we construct an event pattern-instance graph (EPIG) to comprehensively capture the role semantics embedded in various direct and indirect associations, including those among roles within event patterns, arguments within event instances, and the alignments between patterns and instances. Second, to enhance the learning of role node representation in the graph, we optimize the update mechanisms for both node and edge representations in the EPIG graph. By leveraging the graph attention network, we iteratively update the representations of role nodes and role edges. The role representations learned from the EPIG are then integrated into the original role representations, further enriching their semantic information. Finally, a role representation memory module and a multi-round learning strategy is proposed to retain and refine role representations learned from previously analyzed documents. This memory mechanism enhances the prediction performance in subsequent rounds of span selection. Extensive experiments on three datasets verify the effectiveness of the model.