In the application of big data theory, there are many large scale multiple classification problems for the diversity and complexity of real world. However, the hyperplane updating of traditional multiple classification methods are not balanced. And the learning efficiency of them are low, and they are not efficient for the complex multiple classification data. To solve this problem, this paper presents an improved dynamical active multiple classification method (DYA). By combining the definitions of deadlock and activation with the active multiple classification process, the proposed method controls dynamically the status whether the sample is to be involved in the active learning process with the updating of classifier in it. Meanwhile, the active learnin g method with sub-bit counter and rotation learning approach is used to the balance learning and updating of classifier. The experiment results demonstrate that the proposed DYA method can improve both the learning efficiency and generalization performance.