Objective: The minimally invasive treatment of pelvic fractures is one of the most challenging trauma orthopedics surgeries, where preoperative planning is crucial for the performance and outcome of the surgery. However, planning the ideal position of fragments currently relies heavily on the experience of the surgeon. Methods: A pelvic fracture virtual reduction algorithm for target position is provided based on statistical shape models (SSM). First, according to sexual dimorphism, pelvic SSM based on point cloud curvature down-sampling are constructed as adaptive templates. Then, an optimization algorithm is designed to iteratively adjust the target pose of the fragments and the adaptive matching of the templates. Finally, the feasibility of the method is verified by simulating fractures and clinical data. Results: The pelvis has complex shape characteristics, which can be analyzed by SSM to clearly understand the pattern of change. Experiments showed that the SSM-based pelvic fracture reduction method had translation and rotation errors of 2.20±1.09 mm and 3.16±1.26° in simulated cases, and 2.78±0.95 mm and 3.10±0.53° in clinical cases, which has higher accuracy than methods based on mean shape models, and wider applicability than methods based on pelvic symmetry. Conclusion: The pelvic digital model created by SSM has good generalization properties, and the SSM-based virtual reduction algorithm can effectively reconstruct the target position of the fractured pelvis in preoperative planning. Significance: The proposed reduction method has the characteristics of high precision and wide application range, which provides a powerful tool for the surgeon's virtual preoperative planning.