Feature learning has been widely used for image recognition. However, limited training samples and much noise usually make it challenging in practical classification applications. Specifically, it makes sample covariance matrix usually deviate from true ones. To alleviate this bias, we utilize a fractional-order strategy to re-model sample spectra of covariance matrix. On the other hand, as the object classes’ boundary is not very clear in practice, it is necessary to incorporate fuzzy relationship into feature learning. In this paper, we propose a fuzzy fractional canonical correlation analysis (FFCCA), where sample spectra are reconstructed by fractional modeling and at the same time, fuzzy label information is considered. Experimental results on visual recognition have shown that FFCCA can learn more discriminative low-dimensional features, in contrast with existing feature learning methods.