Since the traditional K-nearest neighbor classifier possesses large time and space complexity for larger-scale data sets, prototype selection is an effective processed method which selects representative prototypes (instances) from the original data set for K-nearest neighbor classifier without reducing the classification accuracy. At present, there exist many prototype selection methods. In this paper, based on the existing CURE algorithm, which is difficult to determine the noise points and has bad dispersed of representative points, the shared neighbor density metric is presented to delete noise points and the maximum and minimum distances are employed to obtain scattered representative points, which generates a novel prototype selection methods PSCURE (improved Prototype Selection algorithm based on CURE algorithm). Some numerical experiments are further conducted to show the performance of the proposed prototype selection algorithm compared with other related prototype selection algorithms. The experimental results show that the proposed algorithm not only can select fewer prototypes but also can achieve higher classifier accuracy for almost all the data sets.