[Objective] This paper summarizes key issues, algorithms, and models from the field of Chinese word segmentation, aiming to provide theoretical basis and practical guidance for future research.[Coverage] We reviewed a total of 109 papers from CNKI, Wanfang Data Knowledge Service Platform, and DBLP Computer Science Bibliography.[Methods] First, we discussed the developments and critical issues facing Chinese word segmentation. Then, we explored algorithms and models for Chinese word segmentation. Finally, we identified popular research topics and trends.[Results] The main challenge facing researchers is creating a Multi-Criteria Learning Model for Chinese Word Segmentation with multiple annotation datasets. The most popular research topic is building Multi-task joint model to finish both Chinese word segmentation and other natural language processing tasks.[Limitations] More research is needed to review studies on unsupervised learning approaches for Chinese word segmentation.[Conclusions] The existing methods of Chinese word segmentation still face challenges in building joint models with multi-perspective, multi-task, and multi-criterion features.