Abstract: Aiming at a single fault diagnosis method can′t meet the actual need, a classification model were proposed, which based on rough set-genetic algorithm-neural network algorithm, to come true excavator fault diagnosis classification. Firstly, the attributes were reduced using rough set theory to choose neural network′s input parameters, which reduced the work and calculation time. Then, in order to solve the shortcoming in the back propagation algorithm, such as trapping to the local minimum and slowness in training speed, genetic algorithm was integrated to optimizing the BP network parameters. Finally, the model carried on the training by the reduction results and the optimized BP network parameters. The experimenta1 result shows the effectiveness of the new proposed model.