Entity recognition technology, as an important step in constructing knowledge graphs, has been extensively applied in natural language processing applications such as semantic network, machine translation, and question answering systems. It plays a crucial role in promoting the practical application of natural language processing technology. According to the development process of entity recognition technology, the existing entity recognition methods are investigated in this paper. These methods can be classified as: early rule and dictionary based entity recognition methods, machine learning based entity recognition methods, and deep learning-based entity recognition methods. The core ideas, advantages and disadvantages, and representative models of each entity recognition method are summarized, especially the latest entity recognition methods based on Bi-directional Long Short-term Memory (BiLSTM) and Transformer. Additionally, the current mainstream datasets and evaluation criteria are introduced. Finally, facing the semantic requirements of future machine communication, we have summarized the challenges faced by entity recognition technology, and its future advancement in Internet of Things(IoT) business data is anticipated.