As a typical big data, data stream has the features of continuous, infinite, concept drift and fast arrived. The features make it impossible to apply traditional classification techniques to classify data streams. The paper proposes the concept very fast decision tree(CVFDT) update ensemble(CUE) algorithm based on the classic accuracy weighted ensemble (AWE) algorithm. This algorithm not only improves the weight distribution of the base classifier, but also improves the sensitivity of the block size and the increase of the dissimilarity between base classifiers. Experiments show that, in the classification accuracy, CUE algorithm is higher than the AWE algorithm. Finally, the dynamic classifier selection with clustering (DCSC) algorithm is proposed, which is based on the idea of classifier dynamic selection. The time efficiency is relatively high because there is no tedious weight value mechanism. Experimental results show that the DCSC algorithm can effectively handle the concept of drift and its efficiency is relatively high.