Abstract: On the basis of electrode control in Anyang Steel, a prediction model was established by adopting data mining technique and applied to parameter tuning of an electrode control system. First the data mining process of the electrode prediction model was introduced. A variable structure generic Elman neural network, which can evolve the network structure, the weights and self-feedback gain coefficient simultaneously, was proposed based on a new hybrid generic algorithm and data mining algorithm. The Elman based on BP algorithm and the variable structure generic Elman neural network were applied to establishing of an electrode prediction model for Anyang Steel. The simulation results based on the spot real data of Anyang Steel show that data mining algorithm combined with the variable structure generic Elman neural network has better dynamic characteristic, faster approach speed, better precision than BP algorithm. Finally, when this model was applied to parameter tuning of the electrode control system in Anyang Steel, its control effeet was remarkable.