Aiming at the problems in the existing fuzzy clustering segmentation algorithms, such as poor noise robustness and insufficient image feature extraction, we propose a multi-feature FCM segmentation algorithm combining morphological reconstruction and superpixels. First, the original image is subject to morphological closing reconstruction, which improves the robustness and detail-preserving ability of the algorithm. Secondly, the mean-shift method is employed to pre-segment the reconstructed image and obtain a set of superpixels. Thirdly, the color, texture and gradient features of each superpixel in the reconstructed image are extracted and defined by an averaging strategy to form the multi-dimensional feature vectors. Finally, these vectors are clustered by using the framework of the EWFCM algorithm, taking superpixels as the unit and the nuclear induced distance as the distance measure. Furthermore, six images in the BSDS300 data set are selected for the experimental comparison. The results show that the algorithm in this study has higher segmentation accuracy.