Representation learning is widely used to project high-dimensional data to low-dimensional subspace for feature extraction in image recognition tasks. However, many related methods barely explore the fuzziness and uncertainty between data classes. Besides, the classical unsupervised sparse constraint weakens the evaluation of feature importance and neglects the preservation of discriminant information during sparse representation. To solve these issues, a novel fuzzy discriminative block representation learning (FDBRL) algorithm is proposed for image feature extraction. FDBRL aims to enhance the discriminability of subspace by designing effective constraints for projection learning. Specifically, based on the label information and the fuzzy relation between data, we construct a fuzzy block weight matrix and embed it into the l2,1 norm regularization term to realize supervised sparse constraint for the representation learning. Next, the low-rank constraint is used to capture the inherent global structure information of data. Finally, we introduce a classification loss term with transformation matrix for joint optimization, such that the projection learning is not limited to number of classes, and the discriminative ability is further improved. Comprehensive experimental results on six benchmarks verify that our method achieves promising performance with other state-of-the-arts in both robustness and effectiveness.