Concept drift is an important and difficult issue in streaming data mining tasks. At present， the concept drift processing methods adopt the ensemble learning strategy mostly. However， most of these methods cannot extract the key information of the new data distribution after concept drift， leading to poor model performance. To solve this problem， this paper proposes a concept drift detection and convergence method based on hybrid ensemble of serial and cross （SC_ensemble）. When streaming data are in a stable state， the method trains serial base classifiers for ensemble learning， to extract effective information representing the overall data distribution. After concept drift occurs， parallel cross base classifiers are constructed near the drift site for ensemble learning， to extract the local effective information representing the latest data distribution. By ensemble learning of serial base classifiers and cross classifiers， the method takes into account the overall distribution information contained in streaming data， and strengthens the important local information when concept drift occurs， so that the ensemble model contains more “good but different” base learners， and realizes the efficient combination of learning models after concept drift. The experimental results show that the proposed method can make the online learning model converge quickly after concept drift， and improve the generalization performance of the model.