Due to factors such as uneven pixel response of the detector, mechanical motion of the sensor, and temperature changes during image acquisition, hyperspectral images often contain stripe noise. Current destriping methods often focus on the overall properties of the stripes and ignore their non-local similarity, making it difficult to achieve satisfactory destriping results. To address the above issues, this article proposes a destriping algorithm based on non-local low-rank tensor decomposition and total variation by analyzing the prior information of stripe noise and clean images. This algorithm considers the non-local similarity of stripes, clusters stripes similar to the reference block, and then approximates them using tensor low-rank decomposition. In addition, it also considers the directional and structural sparse characteristics of bandstripes, and achieves effective reduction of spectral distortion by jointly considering the local and non-local similarity of hyperspectral images. To evaluate the destriping effect of this method, we conducted both simulated data experiments and real data (Data from the Gaofen-5 satellite and data captured by the EO-1 Hyperion hyperspectral sensor of the Earth observation satellite in a certain region of Australia) experiments. The results of the simulated data experiments showed that under random length stripes and overall stripes, the mean peak signal-to-noise ratio (MPSNR) and mean structural similarity index measure (MSSIM) values of this algorithm were about 2~3 dB and 0.02~0.04 higher than the best results in the comparison method, respectively, while the mean spectral angle mapper (MSAM) value decreased by about 0.02~0.06. The results of real data experiments show that the algorithm can accurately estimate and separate stripes, recover image information affected by stripes, overcome the problem of residual stripes, and outperform the comparison method in terms of the inverse coefficient of variation (ICV) and mean relative deviation (MRD), which are non-reference evaluation metrics.The algorithm proposed in this article provides an effective solution for removing stripe noise in hyperspectral images, and is expected to provide strong support for the subsequent applications of hyperspectral images.