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In view of the existing combination forecasting methods based on rough set theory that may not be able to weight individual individual forecasting models, the attribute importance in the original method is adjusted and combined with the root mean square error of the individual forecasting model to form a new attribute importance. The new attribute importance is used to determine the combination forecasting weight coefficients, which solves the problem that the original method cannot be weighted, and increases the consideration of forecasting accuracy. Weight coefficients are also determined according to the historical forecasting performance of the models, which reflects the forecasting stability of the models. The integrated weighting method is used to fuse the two kinds of weight coefficients. Based on a certain type of ship maintenance cost data example, the improved method is compared with the commonly used combined forecasting methods, and better results are obtained, the accuracy and stability of the forecasting are improved, and the effectiveness of the method is verified.

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