The syntactic structure of event sentences contributes to semantic understanding. A novel event detection model called BERT(Bidirectional Encoder Representations from Transformers) +D (Dependency)-T(Tree)-LSTM(Long Short-Term Memory network)+D-Attention(BDD) is proposed, which aims to learn semantic and syntactic representation of sentences jointly to enhance the event-sentence understanding ability. Taking the word vector based on BERT as the information source, D-T-LSTM model is designed to integrate the learning of syntactic structure and sentence semantics. An attention mechanism based on the dependency vector is added to strengthen the distinction of different syntactic structures at the aim of event detection. Experiment results on the Chinese Emergency Corpus(CEC) prove the effectiveness of BDD. The precision, recall and F1 value of BDD are rather optimum, and the F1 value is 5.4% higher than that of the benchmark model, and the recall rate is 0.4% higher.