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Event extraction technology mainly studies how to extract event information that users are interested in from unstructured natural language text.It is an important branch in the field of information extraction and has been widely used in intelligence analysis,intelligent question answering,information retrieval,and recommendation systems in recent years.Beginning with the concept and task of event extraction technology,this paper comprehensively reviews the data sets and methods of event extraction technology,analyzes the technical research progress of event extraction tasks,and summarizes event extraction methods based on pattern matching,machine learning,and deep learning.According to the different learning methods of the model and the difference in the size of feature range,it focuses on the introduction of deep learning-based methods,discussing and analyzing both the advantages and disadvantages of different methods.Finally,the challenges and future research trends of event extraction are summarized,and the current research trends are summarized in view of the low resource scenarios,poor generalization ability and difficult modeling of document-level event extraction.