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Feature selection is a basic and important step in real applications, such as face recognition and image segmentation. In this paper, we propose a new weakly supervised multi-view feature selection method by utilizing pairwise constraints, i.e., the <b>p</b>airwise <b>c</b>onstraint-guided multi-view <b>f</b>eature <b>s</b>election (PCFS for short) method. In this method, linear projections of all views and a consistent similarity graph with pairwise constraints are jointly optimized to learning discriminative projections. Meanwhile, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">l</mi><mrow><mn>2</mn><mo>,</mo><mn>0</mn></mrow></msub></semantics></math></inline-formula>-norm-based row sparsity constraint is imposed on the concatenation of projections for discriminative feature selection. Then, an iterative algorithm with theoretically guaranteed convergence is developed for the optimization of PCFS. The performance of the proposed PCFS method was evaluated by comprehensive experiments on six benchmark datasets and applications on cancer clustering. The experimental results demonstrate that PCFS exhibited competitive performance in feature selection in comparison with related models.