Aiming at the problem of low classification accuracy in big data environment, an ensemble classification model for distributed data streams was proposed. Firstly, the microcluster mode was used to reduce the amount of data transmitted from local nodes to the central nodes, so as to reduce the communication cost. Secondly, the training samples of the global classifier were generated by using the sample reconstruction algorithm. Finally, an ensemble classification model for drift data streams was proposed, which adopted the weighted combination strategy of dynamic classifiers and steady classifiers, and the mixed labeling strategy was used to label the most representative instances to update the ensemble model. Experiments on two virtual datasets and two real datasets showed that the model suffered less fluctuation from concept drift compared with two distributed mining models DS-means and BDS-ensemble, and had higher accuracy than Online Active Learning Ensemble model (OALEnsemble), with the accuracy on four datasets improved by 1.58、0.97、0.77 and 1.91 percentage points respectively. Although the memory consumption of this model was slightly higher than those of BDS-ensemble and DS-means models, this model was able to improve the classification performance at a lower memory cost. Therefore, the model is suitable for the classification of big data with distributed and mobility characteristics, such as network monitoring and banking business system.