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Application of alternate covering neural network in data mining is given to the classification algorithms, which overcome the continuous iteration and local minimum of traditional neural network algorithms. The calculation speed is high and it is able to adapt to high-dimensional data classification well. Through case study, intuitive geometric significance of alternate covering is used to structure classification. Comparing with BP neural network algorithm and the decision tree algorithm, it is not iterative and without local minimum, which improves the speed and accuracy of classification. It is concluded that the alternative covering neural network uses parallel processing capability which can achieve rapid calculation in order to adapt to data mining applications.