The maintenance of an aero-engine usually includes three levels ,and the maintenance cost and period greatly differ depending on the different maintenance levels .To plan a reasonable maintenance budget program , airlines would like to predict the maintenance level of aero-engine before repairing in terms of performance parame-ters ,which can provide more economic benefits .The maintenance level decision rules are mined using the histori-cal maintenance data of a civil aero-engine based on the rough set theory ,and a variety of possible models of upda-ting rules produced by newly increased maintenance cases added to the historical maintenance case database are in-vestigated by the means of incremental machine learning .The continuously updated rules can provide reasonable guidance suggestions for engineers and decision support for planning a maintenance budget program before repai-ring .The results of an example show that the decision rules become more typical and robust ,and they are more accurate to predict the maintenance level of an aero-engine module as the maintenance data increase ,which illus-trates the feasibility of the represented method .