Tensor Robust Principal Component Analysis (TRPCA) aims to recover clean tensor data corrupted with noise. This method finds significant application in recovering multidimensional data. However, the majority of existing methods rely solely on local similarity and global information, neglecting the potential benefits offered by the non-local similarity within those data. This oversight often leads to inferior recovery performance. To improve the performance of data recovery, we propose Weight Hankel-TRPCA, which integrates additional prior information to obtain good recovery performance, resulting in improved recovery performance. Specifically, the multidimensional data is divided into three dimensional patches, and similar patches are partitioned to obtain their non-local similarity. The partitioned patches are then projected onto the Hankel tensor to improve their low-rank properties. The algorithm is solved using the famous alternating direction method of multiplier (ADMM). Extensive experimental results show that the proposed method HK-TRPCA outperforms several state-of-the-art methods in terms of performance.