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In this paper classification of data mining based on radial basis function neural networks is researched. After intensive analysis, the training algorithm of radial basis function neural networks is improved in optimum structure, learning speed and approximation accuracy. In learning speed, two-stage learning strategy is used to accelerate the learning process. In approximation accuracy, an error-correction algorithm is presented to improve the output accuracy of radial basis function. In optimum structure, the paper is focused on the number and center selection of the hidden layer units and proposes an adaptive dynamic and static combination algorithm of center selection. Finally, the algorithms are experimented and comparative analyzed. The experimental results show that the performance of the algorithm is significantly improved, and also prove the validity of the improved algorithm.