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Abstract: In order to address the problems that the incomplete data classification algorithms based on imputation strategy have poor performances and cannot characterize the uncertainty caused by missing values, an incomplete data belief classification algorithm based on adaptive KNN imputation (BAI) is proposed. This method yield one or more versions of estimations for a specific incomplete sample, which can not only ensures the accuracy of estimation, but also capture its imprecision. Then, the basic classifier that can implement in complete data well is employed here to classify the edited data with estimations. A new belief classification approach is developed in this paper to assign the indistinguishable object to the proper meta-classes so as to characterize the uncertainty of classification caused by missing values and decrease the risk of misclassification. The real data sets in the UCI are employed to verify the effectiveness of BAI, and the result represents that BAI can effectively handle the problem of incomplete data classification.