A fast and automatic fuzzy C-means clustering (FCM) color image segmentation algorithm is proposed as an alternative to the traditional FCM algorithm, which has high computational complexity and fails to automatically determine the number of clusters. First, the image is presegmented by an improved simple linear iterative clustering (SLIC) algorithm, transforming the traditional pixel-based clustering into superpixel region-based clustering and reducing computational complexity. Second, the improved density peak algorithm determines the number of clusters automatically and improves flexibility. Finally, superpixel images are subjected to histogram-based FCM clustering to complete image segmentation. The BSDS500, AID, and MSRC public databases were utilized as experimental datasets and compared with other FCM segmentation methods to verify their effectiveness. In terms of segmentation accuracy, fuzzy segmentation coefficient, fuzzy segmentation entropy, and visual effect, the experimental results show that the proposed segmentation algorithm outperforms several other comparative algorithms.