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A novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multiplayer backpropagation neural network is structured secondly, the neural network is trained via teacherpsilas guidance thirdly, and the neural network is optimized by genetic algorithm lastly. Based on the ontology of neural network, the data mining algorithm for classified fault diagnosis rules about steam turbine is brought forward; its realization process is as follows: (1) computing the measurement matrix of effect; (2) extracting rules; (3) computing the importance of rules; (4) shearing the rules by genetic algorithm. An experimental system for data mining and fault diagnosis of steam turbine based on neural network and genetic algorithm is implemented. Its diagnosis precision is 84%. And experiments do prove that it is feasible to use the method to develop a system for fault diagnosis of steam turbine, which is valuable for further study in more depth.