For the problem that the fully coupled BP neural network suffers the slow convergence rate to solve the large
scale complex problems, a structure model of function-dividing BP neural network architecture is presented. By using
the physical characteristics of the RBF neurons, the input sample space is decomposed, and different sub-samples space
is sent to different sub-module of BP neural network to learn automatically. Compared with the fully coupled BP neural
network, the searching space of weight in the learning process of neural network is reduced, the learning speed and network’s
generalization performance are improved, and the characteristics of the human brain in the learning proces of knowledge
accumulation are reflected. Experiments of 3D Mexican hat function approximation and two-spiral classification show that
the neural network of function-dividing BP neural network can solve the problem that the fully coupled BP neural network
can not solve perfectly.