The traditional principal curves algorithm can obtain good results on small datasets. But the computing and storage resources of a single node cannot meet the requirements of the extraction of principal curves on massive datasets. Distributed parallel computing is one of the most effective way to solve the problems. Therefore, we proposed a distributed soft K-segments algorithm for principal curves based on MapReduce, named DisSKPC. First, we recursively granulated all the numerical data into information granules to limit each granular size and ensure the relevance of the data in the granules using the distributed K-Means algorithm. Then we calculated the local principal component segments of each granule and eliminated over-fitting segments that may arise in the area of high-density and high-curvature using the noise variance. Finally, we connected these local principal component segments using the Hamiltonian path and greedy algorithm, forming a best curve through the middle of the data cloud. Experimental results demonstrate the feasibility and scalability of the proposed DisSKPC algorithm.