In order to improve the classification accuracy of sweeping learning chain (SLC) algorithm in factor space and solve the problem that SLC is error- prone to classify samples in mixed domain, this paper proposes sweeping learning chain-K-Nearest Neighbor (SLC-KNN) algorithm. When SLC encounters the problem of undivisible data, KNN is used to classify the samples to be tested that fall into the mixed domain. It not only solves the problem of undivisible data encountered by the SLC, but also reduces the amount of computation and storage of KNN algorithm. And extended to multi-classification problem, proposed BT- SLC-KNN multi-classification algorithm. According to the maximum class center distance, firstly the algorithm makes the two most easy to separate classes separate. And defines the Class center distance on sweeping vector, a normal binary tree is generated step by step by comparing the distance of the remaining categories on the sweeping vector of the two classes with the furthest distance. It reduces the time complexity of merge sweeping learning chain (MSLC) algorithm and improves the multi-classification accuracy. Finally, the experiments are carried out on UCI data sets, and the results show that the two algorithms are feasible and effective.