The invention discloses an integrated learning method and device based on interval optimization. The method comprises: step one, one group of trained base classifiers is obtained; step two, predictionvalues of all training samples by the base classifiers are obtained; step three, a weight of each base classifier is optimized by optimized integrated model interval distribution; and step four, weighted average processing is carried out on the prediction values of the samples by using the optimized weights to obtain a final prediction value and mark. In addition, the invention also discloses anintegrated learning device based on interval optimization. According to the invention, dependence on the specific base classification algorithm is avoided; and optimization is carried out based on theclassification error or AUC. Moreover, the whole model is easy to solve; and the applicability is high. During weight optimization, the accuracy and diversity are balanced by introducing the interval, so that the overfitting problem is solved and the prediction effect of the final integrated model is improved.