[Objective] This paper utilizes the SPO triples and the dependency syntax to improve the performance of the event detection model.[Methods] We constructed an event detection model EDMC3S combining the semantic information of SPO triples and the type information of dependency syntactic relationship. First, the model generates SPO triples and dependency syntax relation type weight matrix based on the dependency syntax tree of the sentence. Then, we used a multi-head attention mechanism to strengthen the semantic features of SPO triples and a self-attention mechanism to distribute the weight of different dependency relation types. Third, we extracted the global syntactic and semantic features through the multi-order graph attention aggregation network. Finally, we integrated the semantic features of SPO triples and the global features of statements with a connection layer. [Results] We examined the new model on the ACE2005 dataset, and it achieved better classification performance in the two sub-tasks of trigger word recognition and event type classification. On the three evaluation indexes of P, R, and F1, the recognition of trigger words reached 80.6%, 82.4%, and 81.5%, respectively, and the classification of event types reached 78.7%, 80.1%, and 79.4%, respectively. [Limitations] We need to evaluate the new model with more datasets. [Conclusions] The proposed model can improve the effect of event detection in trigger word recognition and event type classification.
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