One improved correction method of aircraft engines fan speed by variable index factor was proposed based on the similarity theory. The cruise data of an aircraft engine from different airlines were collected to build the data sample. Method of support vector regression machine was used to establish the model of index factor and atmosphere temperature

and the model parameters were optimized based on genetic algorithm. The variable index factor model

consisting of indicated fan speed and atmosphere temperature

was set up to calculate the corrected fan speed. The model was applied by sample data

and the results were compared with those by fixed index factor method

then the error analysis was drawn through the calculation results of two methods. The result indicates that aircraft engine corrected fan speed has higher accuracy through the method of variable index factor and the model has good generalization performance

so it is an effective approach for computing aircraft engine corrected fan speed

providing a practical guidance for the aircraft engine performance prediction.