Natural language processing is an important research direction and research hotspot in the field of artificial intelligence. Named entity recognition is one of the key tasks, which is to identify entities with specific meanings in the text, such as names of people, places, institutions, proper nouns, etc. Traditional named entity recognition methods are mainly implemented based on rules, dictionaries, and statistical learning. In recent years, with the rapid expansion of Internet text data scale and the rapid development of deep learning technology, a large number of deep neural network-based methods have emerged, which have greatly improved the accuracy of recognition. This paper attempts to summarize the traditional methods and the latest research progress in the field of named entity identification, and summarize and analyse its main models, algorithms and applications. Finally, the future development trend of named entity recognition is discussed.