Complexity reliability calculation method based on optimized learning machine

The invention provides a complexity reliability calculation method based on an optimized learning machine. According to the method, firstly, a reliability important influence region is determined; then, new samples are selected to be calculated in a purposive way in the region according to a certain strategy, so the calculation times of a limit state function is reduced to the maximum degree, meanwhile, the limit state function is cyclically rebuilt at high efficiency and high precision, finally, the simulated reliability calculation is fast carried out on the basis of a rebuilt approximate limit state function model by using an important sampling method, and the goal of obtaining high-precision reliability calculation results by calculating the limit state function in fewer times is finally achieved. The defect that the calculation precision and the calculation efficiency cannot be simultaneously considered in the conventional reliability calculation method is overcome, so the practicability of the method in the engineering reliability analysis is improved.