There are several problems that the System Dynamics (SD) model cannot express both random delay and conditional transitions between different states, and the Stochastic Petri-Net (SPN) itself still has the defect of insufficient computing ability. In order to solve the problems, firstly, the SPN was expanded and the Stochastic Function Petri-Net (SFPN) model was proposed. Secondly, combining SFPN with SD, the SFPN-SD model was put forward. Because the transits in SFPN could be used to accurately describe random delay, therefore, the first problems in SD model was solved. Because the conditions arcs in SFPN could be used to express the conditional transferring among places, as a result, the second problem in SD was solved. Finally, some state variables and state transition equations were appended in places and transitions of SPN, while these state variables and state transition equations were the different interpretations of level, auxiliary and rate variables as well as level and rate equations in SD model. The state transition equations could realize complicated computations, and thus the problem of insufficient computing ability in SPN was solved. The SFPN-SD model inherited all the features of the SD model, at the same time, all the features of SPN were incorporated into the SFPN-SD model. Compared with the SD model, the proposed SFPN-SD model has such advantages that system states and the meaning of their types as well as the process of state evolution become more clear. And the system's dynamic changes are driven by events in SFPN-SD, which it can describe autonomous dynamic stochastic evolution of complex system more realistically. The case studies show that, compared with the SD model, the proposed SFPN-SD model has stronger, more comprehensive abilities such as relevancy analysis description and simulation of complex system.