For the problem that the traditional fuzzy clustering is not able to load all the data at a time because of the limited memory in the application scenario for large-scale data, and using a single medoid is insufficient to capture the underlying structure of data and describe each cluster, a fuzzy clustering algorithm based on multiple medoids for large-scale data is presented. The algorithm handles data chunk by chunk, and uses multiple medoids to represent the underlying data structure and each cluster information in one chunk, and the pairwise constraints from the relationship between two identified medoids are taken into account. These mechanisms improve the accuracy of the final clustering results. The effectiveness of the proposed algorithm is verified by three sets of experiments on a simulated dataset and two real datasets.