In order to improve the accuracy and stability of the K-medoids algorithm and solve the problem that the number of clusters of K-medoids algorithm needs to be manually given and is sensitive to the initial cluster center point, we propose an improved K-medoids algorithm based on density weight Canopy. Firstly, we calculate the density value of each sample point in the data set, select the sample point with maximum density value as the first cluster center and remove the density cluster from the data set. Secondly, we select other cluster centers by calculating the weight of the remaining sample points. Finally, the density weight Canopy is used as the preprocessing procedure of the K-medoids and its result is used as the cluster number and initial clustering center of the K-medoids algorithm. The new algorithm is tested on some well-known data sets from UCI real dataset and some artificial simulated data sets. Simulation results show that the new algorithm has higher clustering accuracy and better clustering stability.