The Orthogonal Approximate Message Passing (OAMP) algorithm enhances performance and stability remarkably by integrating orthogonalization, and it exhibits excellent detection performance even in ill-conditioned channel environments. However, the high implementation complexity of the OAMP algorithm, mainly caused by the computation of the decorrelated matrix in the linear estimation segment, poses a problem. To address this issue, we propose a low-complexity OAMP algorithm in this paper. Relying on the sparsity of the channel matrix, the new algorithm simplifies complex calculations of the focused decorrelated matrix into vector operations, significantly reducing computational complexity. In the nonlinear estimation part, error correction capability is improved by adjusting the estimations with the decorrelated matrix, and a damping factor is introduced to accelerate convergence. Experimental results demonstrate that the low-complexity OAMP algorithm presented in this paper not only successfully reduces algorithmic complexity but also achieves remarkable performance improvements.