Event Argument Extraction involves identifying event-related arguments in text and determining their respective roles. Recent mainstream methods for event argument extraction still fall short in handling long-distance dependencies of arguments, resulting in limited contextual understanding. Therefore, we propose DSEAE, an effective model for event argument extraction. The DSEAE model introduced herein comprises two primary components: a dependency-guided module and a structure-aware module, with each integrating a unique and enhanced self-attention mechanism. The dependency-guided module aims to help the model relate each prompt to the relevant event context, whereas the structure-aware module aims to strengthen interaction between event information for better contextual understanding. Our experiments indicate that DSEAE outperforms the baselines, underscoring its effectiveness.