In this study, we developed an ensemble model O-GlcNAcPRED-II to identify potential O-G1cNAcylation sites. A K-means principal component analysis oversampling technique (KPCA) and fuzzy undersampling method (FUS) were first proposed and incorporated to reduce the proportion of the original positive and negative training samples. Then, rotation forest, a type of classifier-integrated system, was adopted to divide the eight types of feature space into several subsets using four sub-classifiers: random forest, k-nearest neighbour, naive Bayesian and support vector machine. We observed that O-GlcNAcPRED-II achieved a sensitivity of 81.05%, specificity of 95.91%, accuracy of 91.43% and Matthew’s correlation coefficient of 0.7928 for five-fold cross-validation run 10 times. Additionally, the results obtained by O-GlcNAcPRED-II on two independent datasets also indicated that the proposed predictor outperformed five published prediction tools.