Aiming at the problem that existing machine reading comprehension-based event detection models are difficult to mine the long-distance dependencies between keywords when facing long text context with complex syntactic relations， a Machine Reading Comprehension event detection model based on Relation-Enhanced Graph Convolutional Network （MRC-REGCN） was proposed. Firstly， a pre-trained language model was utilized to jointly encode the question and the text to obtain word vector representations incorporating the priory information. Secondly， the dynamic relationship was introduced to enhance the label information， and an REGCN was utilized to deeply learn syntactic dependencies between words and enhance the model’s ability to perceive the syntactic structure of long text. Finally， the probability distributions of the textual words under all event types were obtained by the multi-classifier. Experimental results on the ACE2005 English corpus show that the F1 score of the proposed model on trigger classification is improved by 2.49% and 1.23% compared to the comparable machine reading comprehension-based model named EEQA （Event Extraction by Answering （almost） natural Questions） and the best baseline model named DEGREE （Data-Efficient GeneRation-based Event Extraction） respectively， which verify that the MRC-REGCN has a better performance in performing event detection.