As a core task and an important link in the fields of natural language understanding and information retrieval, information extraction (IE) can structure and semanticize unstructured multi-modal information. In recent years, deep learning (DL) has attracted considerable research attention to IE tasks. Deep learning-based entity relation extraction techniques have gradually surpassed traditional feature- and kernel-function-based methods in terms of the depth of feature extraction and model accuracy. In this paper, we explain the basic concepts of IE and DL, primarily expounding on the research progress and achievements of DL technologies in the field of IE. At the level of IE tasks, it is expounded from entity relationship extraction, event extraction, and multi-modal information extraction three aspects, and creates a comparative analysis of various extraction techniques. We also summarize the prospects and development trends in DL in the field of IE as well as difficulties requiring further study. It is believed that research can be carried out in the direction of multi-model and multi-task joint extraction, information extraction based on knowledge enhancement, and information fusion based on multi-modal at the method level. At the model level, further research should be carried out in the aspects of strengthening theoretical research, model lightweight, and improving model generalization ability.