Context:　Code　readability　plays　a　critical　role　in　software　maintenance　and　evolvement,　where　a　metric　for　classifying　code　readability　levels　is　both　applicable　and　desired.　However,　most　prior　research　has　treated　code　readability　classification　as　a　binary　classification　task　due　to　the　lack　of　labeled　data.　Objective:　To　support　the　training　of　multi-class　code　readability　classification　models,　we　propose　an　enhanced　data　augmentation　approach.　Method:　The　approach　includes　the　use　of　domain-specific　data　transformation　and　GAN-based　data　augmentation.　By　virtue　of　this　augmentation　approach,　we　could　generate　sufficient　readability　data　and　well　train　a　multi-class　code　readability　model.　Result:　A　series　of　experiments　are　conducted　to　evaluate　our　augmentation　approach.　The　experimental　results　show　that　a　state-of-the-art　multi-class　code　readability　classification　accuracy　of　68.0%　is　reached　with　a　significant　improvement　of　6.3%　compared　to　only　using　the　original　data.　Conclusion:　As　an　innovative　work　of　proposing　multi-class　code　readability　classification　and　an　enhanced　code　readability　data　augmentation　approach,　our　method　is　proved　to　be　effective.　©　2022　Knowledge　Systems　Institute　Graduate　School.　All　rights　reserved.