Feature selection is important in data mining, especially in mining high-dimensional data. In this paper, a random subspace based semi-supervised feature selection (RSSSFS) method with pairwise constraints is proposed. Firstly, several graphs are constructed by different random subspaces of samples, and then RSSSFS combines these graphs into a mixture graph on which RSSSFS does feature selection. The RSSSFS score reflects both the locality preserving power and pairwise constraints. We compare RSSSFS with Laplacian Score and Constraint Score algorithms. Experimental results on several UCI data sets demonstrate its effectiveness.