While ensemble learning has achieved remarkable success in generalization performance, the error analysis of ensemble learning needs further research. As cross-validation has an important application for model performance evaluation in statistical machine learning, block-3×2 cross-validation and k-fold cross-validation are applied to integrate the weighted prediction values for each sample point and analyze the error. Experiments on simulated data and real data show that the prediction error of ensemble learning based on block-3×2 cross-validation is smaller than that of a single learner, and the variance of ensemble learning is smaller than that of a single learner. The generalization error of the ensemble learning based on block-3×2 cross-validation is less than that of the one based on k-fold cross-validation, which indicates that the ensemble learning model based on block-3×2 cross-validation has good stability.