Abstract:Medical named entity recognition refers to the extraction of key information from massive unstructured medical data, which provides a foundation for the development of medical research and the popularization of smart medical systems. Deep learning uses deep nonlinear neural network structures to learn complex and abstract characteristics, which can represent data more essentially. Deep learning models can significantly improve the effect of medical named entity recognition. First, this study introduces the unique difficulties and traditional methods of medical named entity recognition. Then, it summarizes models based on deep learning and popular model improvement methods, including the improvement of feature vectors and the ways to deal with difficulties such as a lack of data and the recognition of complex named entities. Finally, the study provides an outlook on future research direction through a comprehensive discussion.