As a core strategic technology industry, the wind power plays an important role in protecting national energy reserves. The large gear component is one of the core foundation parts in wind turbines, and its quality indexes greatly affect the service performance of the wind turbine drive chain and even the wind turbine as a whole. This paper calculates the fatigue load history of the wind power large gear system under the coupling mechanism of elastic behavior based on a multidimensional finite element method, and obtains the probabilistic fatigue strength of gear teeth through the gear low circumference fatigue test and life distribution transformation method, and deeply explores the inherent characteristics of the wind power gear system in functional implementation and then establishes a system fatigue reliability evaluation model. Finally, a mapping path from the global structural elements of the wind power gearbox to the reliability indexes of the gear system is constructed with significant simulation and test cost advantages. It can provide structural optimization guidance in the development and design of large wind power gear systems, and significantly reduce the cost of achieving reliability indexes in the design iterations of such large high-end equipment. At the same time, it can provide cost-effective training data for intelligent optimization algorithms such as the deep reinforcement learning, which will eventually achieve multi-objective optimal stiffness matching for wind power gearboxes under reliability index constraints.