Density peaks clustering (DPC) is a novel clustering algorithm based on density, which has simple principle and high efficiency. However, the definition of local density of samples in DPC only considers the distance between samples and ignores the environment of samples, which leads to the unsatisfactory clustering effect of the algorithm for data with uneven density distribution. At the same time, the process of sample allocation is easy to produce the joint effect of allocation errors. To solve the above problems, this paper proposes a density peaks clustering algorithm based on relative density estimating and multi cluster merging(DPC-RD-MCM). The DPC-RD-MCM defines the local density of the relative K-nearest neighbor based on the idea of the K-nearest neighbor and relative density, so as to reduce the influence of cluster density on the selection of cluster centers and avoid the absence of cluster centers in sparse regions. The similarity measure between micro clusters is redefined, and the final clustering result is obtained by the multi cluster merging strategy, which avoids the joint effect of allocation errors. The DPC-RD-MCM is compared with DPC and its improved algorithm on uneven density datasets, complex morphological datasets and UCI datasets. The experimental results show that the DPC-RD-MCM can achieve excellent clustering effect on uneven density datasets, and the clustering performance of complex morphological datasets and UCI datasets is higher than other comparison algorithms.