Recently, the team published a conference paper titled 《Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition at the Conference on Computer Vision and Pattern Recognition 2023 . PhD student Shun Fang is the first author, Shiqian Wu is corresponding author, and Zhengqin Xu and Shoulie Xie are collaborators. A new RPCA algorithm based on block Krylov iteration and CUR decomposition (eRPCA) is proposed in this paper. Specifically, the Krylov iteration method is used to approximate the eigenvalue decomposition, and the CUR decomposition is used to replace the SVD to update the low-rank matrix components. The experimental results show that the running speed is improved by an order of magnitude compared with the traditional method, which lays a foundation for the practical application of RPCA.