AdaBoost algorithm is a typical ensemble learning framework. It linearly combines a set of weak classifiers to construct a strong one, whose classification accuracy, generalization error and training error are all improved. However, the AdaBoost algo rithm is weak interpretability since it cannot simplify weak classifiers from output model. Hence, one presents a new algorithm, ensemble evolve classification algorithm for controlling the size of final model (ECSM), by introducing the genetic algorithm into the AdaBoost algorithm model. Gene evolution and fitness function can mandatory reserve the species diversity of samples in the AdaBoost iteration framework, and leave better classifiers. With keeping the classification accuracy, experimental results show that the proposed algorithm greatly reduce the number of classifiers compared with the classical AdaBoost algorithm.