Data stream is characterized by the continuous arrival of data, the unpredictability of the time of concept drifts and the uncertain number of concepts, making it difficult to predict the size of the window in advance, but the number of concepts in the window affects the detection of concept drifts. This paper proposed an algorithm for the fast detection of concept drifts in data streams by taking a confident majority voting strategy (CMV_SEA). This algorithm replaced base classifiers in a window like SEA and used majority voting strategy to ensemble all base classifiers in the window. The experiment results illustrate that CMV_SEA can promote prediction accuracy and detect concept drifts as soon as a new concept comes up, and its ability to detect and learn a new concept is not influenced by the size of the window.