In view of soft subspace clustering with feature weight self-adjustment mechanism (SC-FWSA) clustering algorithm sensitive to noise, based on a non-Euclidean distance, a robust soft subspace clustering with feature weighting self-adjustment mechanism (RSC-FWSA) was proposed. RSC-FWSA algorithm adaptively generated a weighting function for data during the iteration, and computed the clustering centers by computing the weighted average of each class. And this "weighted average" made the estimation of the cluster centers be relatively insensitive to noise, and improved the clustering accuracy of algorithm for data with noise and complex structure. The effectiveness of RSC-FWSA algorithm were demonstrated with comparative experiments on synthetic and real data. Especially the experimental results on synthetic data set with noise and 3 real data sets:Wine, Zoo and Breastcancer show that RSC-FWSA can significantly improve the clustering accuracy compared to original corresponding algorithm. RSC-FWSA has strong robustness, which makes it be suitable for the clustering of data with high dimensions, noise and complex structure.