Feature learning is an important technique in machine learning, which studies data representation learning required by the post task from raw data. At present, most feature learning algorithms focus on learning topological structure of the original data, but ignore the discriminant information in the data. This study proposes a novel model called discriminant feature learning based on t-distribution stochastic neighbor embedding (DTSNE). In this model, the learning of discriminant information and the learning of topology structure are fused together, so both of them are learned to obtain the discriminant feature representation of the original data through iterative solution, which can significantly improve the performance of the machine learning algorithm. Experimental results on multiple open data sets demonstrate the effectiveness of the proposed model.