Clustering is an important method for data analysis in the field of data mining. The function of clustering is to divide unlabeled data divided into several groups according to the data similarity. CSDP is a density-based clustering method. When data size is large or data dimensionality is high, the efficiency of clustering is relatively low. In order to improve the efficiency of clustering algorithm, this paper proposes a density-based distributed clustering method, called MRCSDP, which uses MapReduce to cluster text data. This method introduces the definition of independent calculation unit and independent calculation block. First, data are split into several data blocks which are used to construct independent calculation unit and independent calculation block. The task for each independent calculation block is assigned. Then the distributed calculation is conducted to obtain the local density of the data blocks. The local densities are combined to obtain the global density. The center value is calculated according to the global density. Based on the global density and the center value, the candidate cluster centers of each data block can be obtained. Finally, the global cluster centers are obtained by calculating the density of all candidate cluster centers. MRCSDP can achieve better clustering performance by reducing time complexity. Experimental results show that compared to CSDP, MRCSDP can process large scale data more effectively with load-balancing on each computing nodes.