In neural network intelligent (NN) diagnosis, the knowledge which is acquired by NN, is very difficult to be explained and understood, therefore, the further application of NN intelligent diagnosis is limited. In this paper, the rules extraction method from neural network based on the functional point of view is studied, and the key algorithms are introduced, such as the discretization of continuous attributes, the generation of train samples of neural network (NN), the training of NN, the generation of the instance samples from the trained NN, and rule extraction. The new method is compared with the other methods, and its correction is verified. Finally, the fault experimentation samples are obtained by a multi-function rotor experimental rig, the rules extraction method is used to extract the diagnosis knowledge rules from fault samples, and the results show the correctness and rationality of the new method.