In order to solve the problems of long classification time and low mining accuracy of current data mining models,a data mining model based on improved decision tree classification algorithm(SPRINT)is proposed.Firstly,the maximum-minimum normalization formula is used to complete the linear transforma-tion of the original data,and the improved SPRINT classification algorithm is used to classify the input data according to the characteristics of the input data.The collaborative filtering technology is used to generate the attribute set similar to data,and calculate data attribute similarity to generate semantic rule set,which could provide users with better data services.A company's marketing data set is selected as a sample for comparative experiments.The results show that,compared with the comparative model,the proposed data mining model has shorter classification time and higher mining accuracy,which can provide users with bet-ter data services.
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