Traditional multidimensional scaling method achieves low-dimensional embedding， which maintains the topological structure of data points but ignores the discriminability of the low-dimensional embedding itself. Based on this， an unsupervised discriminative feature learning method based on multidimensional scaling method named Discriminative MultiDimensional Scaling model （DMDS） was proposed to discover the cluster structure while learning the low-dimensional data representation. DMDS can make the low-dimensional embeddings of the same cluster closer to make the learned data representation be more discriminative. Firstly， a new objective function corresponding to DMDS was designed， reflecting that the learned data representation could maintain the topology and enhance discriminability simultaneously. Secondly， the objective function was reasoned and solved， and a corresponding iterative optimization algorithm was designed according to the reasoning process. Finally， comparison experiments were carried out on twelve public datasets in terms of average accuracy and average purity of clustering. Experimental results show that DMDS outperforms the original data representation and the traditional multidimensional scaling model based on the comprehensive evaluation of Friedman statistics， the low-dimensional embeddings learned by DMDS are more discriminative.