High-quality ghost imaging (GI) under low sampling is very important for scientific research and practical application. How to reconstruct high-quality image from low sampling has always been the focus of ghost imaging research. In this work, based on the hypothesis that the matrix stacked by the vectors of image's nonlocal similar patches is of low rank and has sparse singular values, we both theoretically and experimentally demonstrate a method that applies the projected Landweber regularization and blocking matching low-rank denoising to obtain the excellent image under low sampling, which we call blocking matching low-rank ghost imaging (BLRGI). Comparing with these methods of "GI via sparsity constraint," "joint iteration GI" and "total variation based GI," both simulation and experiment show that the BLRGI can obtain better ghost imaging quality with low sampling in terms of peak signal-to-noise ratio, structural similarity index and visual observation.