Air cargo and mail turnover is an important index to evaluate the development of aviation logistics industry. Traditional prediction methods perform poorly in the prediction of complex systems. Therefore, based on the trend extrapolation method, polynomial regression and grey prediction, this paper introduces the induced ordered weighted average operator combination prediction model. The results show that this method has a better forecasting effect on air cargo and mail turnover than other methods, and the prediction error is only 4.72% when it is tested with 2018 global data. The status and future trends of the global and Chinese air cargo and mail markets is further compared and analyzed through the prediction results, which will provide some suggestions and implications for the upgrading of China's aviation logistics industry and the optimization and development of the national economic structure.