Most data-driven soft sensors assume that the processes operate in a steady state, which may be improper because of the essential dynamics in the process industries. Because of commonly existing irregular quality samples, establishing dynamic soft sensor models is a difficult task. To cope with this problem, a nonlinear dynamic soft sensor model with a Wiener structure is proposed in this paper. Such a structure consists of two parts: (i) finite impulse responses of first-order transfer functions with dead time are introduced to approximate the dynamic properties and (ii) a nonlinear network is utilized to describe the nonlinearity. An iterative two-level optimization method is applied to establish this dynamic soft sensor model. The computational cost is reduced, and convergence can be guaranteed. The proposed dynamic soft sensor approach is validated through simulation and industrial case studies.