This paper proposes a meta-classification method for one-class classification models by representing them as normality rankings and using ranking correlation and nearest neighbor metrics. The approach achieves high accuracy in classifying models based on training datasets, algorithms, and hyperparameters, and works even when datasets share the same class. The method effectively classifies datasets by treating multiple samples as a single input, offering a unified solution for OCC models, datasets, and rankings.