Density-based spatial clustering of applications with noise （DBSCAN） can find clusters of different densities and sizes， is also robust to noise， and is widely used in data mining tasks. DBSCAN needs to adjust the parameters MinPts and Eps to achieve a better clustering effect， but it often affects the performance of DBSCAN in the process of searching for the optimal parameters. This article optimizes DBSCAN from two aspects. On one hand， a parameter-free method is proposed to optimize DBSCAN global parameter selection. The parameter-free method uses the natural nearest neighbor to obtain the natural feature value of the data set， and uses the natural feature value as MinPts. Then， the natural feature set is calculated according to the natural feature value， and three strategies （i.e. statistics of minimum， mean and maximum） are used to obtain the Eps values by using the data distribution characteristics of the natural feature set. On the other hand， it uses the graphics processing unit （GPU） of the real-time acceleration platform for integrated data science （RAPIDS） platform to accelerate the convergence of DBSCAN algorithm. The experimental results show that the proposed method can optimize DBSCAN parameter selection while obtaining the comparable clustering results of density peaks clustering （DPC） algorithm.