Nonnegative low-rank matrix approximation is an important technique in data analysis for extracting meaningful patterns from high-dimensional nonnegative data. This nonnegative low-rank approximation problem is studied and a new blocksplitting method is developed in this talk. This new method enforces the low-rank constraint by utilizing QR decomposition and adopts a semismooth Newton method to address the related convex subproblems efficiently through the dual formulation of the nonnegative low-rank matrix approximation problem. Theoretical analysis confirms the convergence of the new method. Several real datasets are used to demonstrate the efficiency of the proposed method.