Inspired by insects and birds which achieve outstanding aerodynamic performance using flapping motions, many researchers have been trying to develop flapping-wing micro air vehicles (MAVs). Basic experiments have been conducted to investigate the aerodynamics of flapping motions. However, because there are numerous parameters in the kinematics and morphology, it is difficult to experimentally determine the parameter values that influence flapping aerodynamics. Furthermore, time and cost are constraints in experiments. Therefore, simulation-based approaches are getting attention. For the analysis of flapping motions, 3D fluid-structure interaction (FSI) simulation with very large number of degrees of freedom is required. Therefore, it is very time consuming, and thus a speed-up is important. For such a purpose, we previously developed a 3D parallel FSI analysis system to investigate flapping motions. However, to perform an intensive parametric study for a wide range of combinations of various flapping motions such as flapping, pitching and lead-lag, it is necessary to further accelerate a 3D FSI analysis. To enhance computational efficiency of the high-fidelity numerical models, reduced-order modeling (ROM) techniques have been developed in recent years. For problems with parametrized systems in extremely high-dimension, lower-dimensional manifolds with representative key features are to be sought. With sufficient training datasets (i.e., snapshot data) collected from the ‘offline’ high-fidelity analysis, data compression techniques such as proper orthogonal decomposition (POD) can be applied to obtain reduced-order bases. Such a low-dimensional representation of the original high-dimensional system can be developed to significantly accelerate the ‘online’ predictive simulation while properly capturing main characteristics of the original complex system. The POD method has been widely applied in conjunction with Galerkin projection to build reduced-order models for a wide range of engineering problems, such as fluid dynamics, and thermomechanical dynamics, among others. Although some papers on POD-based ROM for FSI analysis have been recently published, application examples are limited to simple FSI problems like Turek-Hron FSI benchmark. In the present study, we propose a ROM method for an FSI analysis system based on arbitrary Lagrangian-Eulerian method. Then, we apply the proposed method to the FSI problem of flapping motions.