Abstract: The construction of ensemble learning algorithms is one of the important contents in machine learning area. The weak learning theorem proves that the weak learning algorithm is equal to the strong one essentially, but how to construct a good ensemble learning algorithm is still a problem to be studied. Freund and Schapire’s AdaBoost boosting algorithm, and Schapire and Singer’s real AdaBoost boosting algorithm partially solved this problem. A concept of learning error is defined. Based on it, aiming at the minimization of learning error, a universal ensemble learning algorithm is put forward. By using it, the learning error can decrease while increasing simple predictions. This universal ensemble learning algorithm can solve almost all classification problems, such as the multi-class classification problem, the cost-sensitive classification problem, the imbalanced classification problem, the multi-label classification problem, the fuzzy classification problem, etc. The universal ensemble learning algorithm can unify a series of AdaBoost algorithms and has generalized AdaBoost algorithm also. It is put forward that the simple prediction in all algorithms above can be constructed by single characteristics based on samples for the generalization ability of the ensemble prediction. Theoretical analysis and experimental conclusion all show that this universal ensemble learning algorithm can get any small learning error, and it is not easy to cause over-learning phenomenon.