For fixed-point smoothing estimation problems in the non-Gaussian environment, this paper proposes a smoothing estimation algorithm based on maximum correntropy as the optimal criterion, which is called fixed-point maximum correntropy smoother(FP-MCS). First, an alternate form of maximum correntropy Kalman filter(MCKF) is given based on matrix transformation. Then, new states are introduced to augment the system, and online iterative equations of the proposed FP-MCS are derived through the new MCKF form. Furthermore, state estimation error covariances are compared before and after smoothing, and performance improvement of the proposed FP-MCS is analyzed theoretically. Meanwhile, its computational complexity is also compared with other algorithms. Finally, an illustrative example is presented to verify the effectiveness and superiority of the proposed FP-MCS in the non-Gaussian mixture noise environment.