Aiming at the problems of insufficient utilization of syntactic dependencies between words and lack of global semantic information in Chinese event detection， a Chinese event detection model based on syntactic and full-text information enhancement is proposed. Firstly， the model introduces graph convolutional network （GCN） to enhance the feature representation of words by capturing the dependency syntactic relationship between words. Then， bidirectional gate recurrent unit （Bi-GRU） is used to learn the context information within and between sentences respectively， and the sentence vector containing the global information of the article is obtained. Finally， the information of word， phrase and sentence is dynamically fused through the gate structure， and the conditional random field （CRF） is used to identify and label the trigger words in the sentence. Experimental results on ACE2005 and CEC Chinese data sets show that the proposed method effectively improves the effect of Chinese event detection.