Abstract: Event detection aims to identify event trigger words in text and classify them into predefined event types. Existing methods still face significant challenges in modeling long-distance dependencies and handling overlapping and nested events. To address these issues, this paper proposes a context-enhanced event detection model that integrates direction-aware edge updating and argument combination to better representing complex events. Specifically, based on traditional graph convolutional networks, it introduces a direction-aware edge updating module to effectively capture asymmetric dependency paths and key information within syntactic structures. It also designs a context enhancement module based on argument combination, which fully leverages the sentence-level global context, models syntactic information corresponding to different event types, and handles overlapping and nested events. The proposed model achieves F₁ scores of 84.46% and 70.11% on the ACE-2005 and ERE-EN datasets, respectively, outperforming the best baseline models on each dataset by 1.66% and 5.11%. Compared with the strong baseline GCB-PFCE, it shows a 3.64% improvement in overlapping and nested events, indicating the model’s effectiveness and robustness in modeling complex event structures.