Event extraction is to automatically extract event information that users are interested in from unstructured natural language text and express it in a structured form. Event extraction is an important direction in natural language processing and understanding, and has high application value in different fields such as government public affairs management, financial business, and biomedicine. According to the degree of dependence on manually labeled data, the current event extraction methods based on deep learning are mainly divided into two categories: supervised learning and distant supervised learning. This article provides a comprehensive overview of current event extraction techniques in deep learning. Focusing on supervised methods such as CNN, RNN, GAN and GCN and distant supervision, and the research in recent years is systematically summarized. Besides, the performance of different deep learning models is compared and analyzed in detail. Finally, the challenges facing event extraction are analyzed, and the research trends are forecasted.