In the traditional ensemble classification algorithm， the ensemble number is generally set to a fixed value， which may lead to a low classification accuracy. Aiming at this problem， an accuracy Climbing Ensemble Classification Algorithm （C-ECA） was proposed. Firstly， the base classifiers was no longer replaced the same number of base classifiers with the worst performance， but updated based on the accuracy in this algorithm， and then the optimal ensemble number was determined. Secondly， on the basis of C-ECA， a Dynamic Weighted Ensemble Classification Algorithm based on Climbing （C-DWECA） was proposed. When the base classifier was trained on the data stream with different features， the best weight of the base classifier was able to be obtained by a weighting function proposed in this algorithm， thereby improving the performance of the ensemble classifier. Finally， in order to detect the concept drift earlier and improve the final accuracy， Fast Hoffding Drift Detection Method （FHDDM） was adopted. Experimental results show that the accuracy of C-DWECA can reach up to 97.44%， and the average accuracy of the proposed algorithm is about 40% higher than that of Adaptable Diversity-based Online Boosting （ADOB） algorithm， and is also better than those of other comparison algorithms such as Leveraging Bagging （LevBag） and Adaptive Random Forest （ARF）.