Hyperspectral images are usually contaminated by Gaussian noise， impulse noise， dead lines and stripes. So， denoising is an essential step. The existing denoising methods based on low-rank characteristics introduce spatial information to improve the noise reduction effect. But because they often only use local similarity or non-local self-similarity， it has poor removal effect of sparse noise with structural information in the spectral dimension. Therefore， we propose a hyperspectral image denoising method based on superpixel block clustering and low-rank characteristics. The method realizes the adaptive partition and clustering of blocks， and makes full use of the non-local spatial self-similarity while retaining the local details. The experiments show that the same object block composed of clustered superpixel blocks has a good spatial-spectral dual low-rank attributes. Firstly， a superpixel segmentation method is applied to hyperspectral images， and the superpixel blocks are clustered to obtain the same object blocks. Secondly， the low-rank matrix restoration model is established and solved， and finally the denoised image is obtained. We conduct experiments on simulated data and real data respectively， and compare with other methods based on low-rank characteristics. The results show that this method has better denoising performance for mixed noise， especially sparse noise with structural information.