[Objective] This paper reviews the current developments and challenges facing term recognition studies based on deep learning. [Coverage] We searched the 中国知网 and the Web of Science using queries of 主题=“术语识别”+“术语抽取”, and subject = “(extract terms OR term recognition OR technology detection OR relation classification) AND deep learning AND ner”. A total of 73 articles were retrieved. [Methods] We reviewed these studies on the general framework of deep learning-based term recognition, model selection, advantages and disadvantages of various models, and future development trends. [Results] Deep learning-based term recognition methods can be categorized into three major types: single neural network models, composite neural network models, and models combining deep learning. BiLSTM-CRF models are the mainstream method for term recognition, with BERT and its optimized models being recent research hotspots. In specific domains, multi-task models are preferred over neural network models, and the application of transfer learning and active learning has become a new research direction. [Limitations] We only conducted a structured analysis of different models and training results of existing studies, lacking a comparison of training effects of different models on the same dataset, requiring further research in the future. [Conclusion] Future research in deep learning-based term recognition should focus on term annotation patterns, integrating multidimensional features of terms, term recognition techniques for small or zero datasets, cross-domain model generalization, interpretability of results, and improvement of evaluation methods.