To resolve the similarity analysis of massive datasets, a complex simulation model validation method based on ensemble learning is proposed. The similarity analysis between simulated time series and observed time series is formulated as a similarity degree classification problem. Machine learning techniques, including back propagation neural network, error correcting output coding support vector machine (ECOC-SVM) and ensemble learning, are utilized to construct an ensemble classification system (ECS). Improve the diversity among base classifiers is essential for building high performance ECS. A screening criterion based on the punish factor is designed to choose base classifiers with maximum diversity. Finally, the proposed model validation method is examined with an application example.