Event detection is one of the most important tasks in the field of natural language processing （NLP）. Its result is the key information supporting downstream tasks， such as information extraction， text classification and event reasoning. BERT model has achieved remarkable achievements in event detection. However， it cannot effectively obtain long-distance and structured text information. To alleviate this problem， feedback-based GCNs network is proposed to capture text structure information in this paper， and it can solve the problem of semantic information attenuation caused by GCNs. This paper first uses BERT model to obtain semantic features of the text， then adopts GCNs integrated into the feedback network to extract the syntactic structure features of the text， and finally employs multiple classifiers to identify and classify event trigger words. The experimental results on the open dataset ACE 2005 show that the F1 value of the event detection method proposed in the task of event trigger word recognition and classification has reached 74.46% and 79.49%， respectively， which gains an average increase of 4.13% and 4.79% compared with the existing work.