Density peak clustering (DPC) is a novel clustering algorithm based on density and distance. It is widely used in the field of data mining because of its simple principle, no iteration and the ability to process shape datasets. However, DPC algorithm also has some defects, such as the cutoff distance parameter is sensitive, the selection of initial clustering center is not automatic, and there is a chain problem in subsequent allocation, with high time complexity. This paper summarizes and arranges the research status of DPC algorithm. Firstly, it introduces the principle and process of DPC algorithm; Secondly, in view of the deficiencies of DPC algorithm, the optimization of DPC algorithm is summarized and analyzed, and points out the core technology, advantages and disadvantages of the optimization algorithm; Finally, the possible challenges and development trend of DPC algorithm in the future are prospected.