Image clustering is a difficult task with important application value in computer vision. The key to this task is the quality of images features. Most of current clustering methods encounter the challenge. That is, the process of feature learning and clustering operates independently. To address this problem, several researchers have been dedicated to performing feature learning and deep clustering together. However, the obtained features lack discriminability to address high-dimensional data successfully. To deal with this issue, we propose a novel model named as robust deep fuzzy K-means clustering (RD-FKC), which efficiently projects image samples into a representative embedding space and precisely learns membership degrees into a combined framework. Specifically, RD-FKC introduces Laplacian regularization technique to preserve locality properties of data. Moreover, by using an adaptive loss function, the model becomes more robust to diverse types of outliers. Furthermore, to avoid the latent space being distorted and make the extracted features retain the original information as much as possible, the model introduces reconstruction error and adds regularization to network parameters. Finally, an effective algorithm is derived to solve the optimization model. Numerous experiments have been conducted, illustrating the advantages and superiority of RD-FKC over existing clustering approaches.