Density Peaks Clustering (DPC) algorithm is a new clustering algorithm with the advantages such as few adjustment parameters, no iterative solution and the capacity of finding non-spherical clusters. However, there are some disadvantages of the algorithm: the cutoff distance cannot be adjusted automatically, and the cluster centers need to be selected manually. For the above problems, an Adaptive DPC (ADPC) algorithm was proposed， the adjustment of adaptive cutoff distance based on Gini coefficient was realized, and an automatic acquisition strategy of clustering centers was established. Firstly, the calculation formula of cluster center weight was redefined by taking local density and relative distance into account at the same time. Then， the adjustment method of adaptive cutoff distance was established based on Gini coefficient. Finally, according to the decision graph and cluster center weight sort graph, the strategy of automatically selecting cluster centers was proposed. The simulation results show that, the ADPC algorithm can automatically adjust the cutoff distance and automatically acquire the clustering centers according to the characteristics of problem, and obtain better results than several commonly clustering algorithms and improved DPC algorithms on the test datasets.