Event extraction is to automatically extract event information in which users are interested from unstructured natural language texts and express it in a structured form. Event extraction is an important direction in natural language processing and understanding and is of high application value in different fields, such as government management of public affairs, 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 distantly-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, GCN, and distant supervision, this study systematically summarizes the research in recent years. Additionally, 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.