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，including the sensitive cutoff distance parameter, nonautomatic selection of initial clustering center, the chain problem in subsequent allocation and high time complexity. This paper summarizes 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 the core technology, advantages and disadvantages of the optimization algorithm are pointed out. Finally, the possible challenges and development trend of DPC algorithm in the future are concluded.