Being　a　critical　factor　affecting　the　maintainability　and　reusability　of　the　software,　code　readability　is　growing　crucial　in　modern　software　development,　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.　To　support　the　training　of　multi-class　code　readability　classification　models,　we　propose　an　enhanced　data　augmentation　approach　that　could　be　used　to　generate　sufficient　readability　data　and　well　train　a　multi-class　code　readability　model.　The　approach　includes　the　use　of　domain-specific　data　transformation　and　GAN-based　data　augmentation.　We　conduct　a　series　of　experiments　to　verify　our　augmentation　approach　and　gain　a　state-of-the-art　multi-class　code　readability　classification　performance　with　69.5%　Micro-F1,　54.0%　Macro-F1　and　67.7%　Macro-AUC.　Compared　to　the　results　where　no　augmented　data　is　used,　the　improvements　on　Micro-F1,　Macro-F1　and　Macro-AUC　are　significant　with　6.9%,　11.3%　and　11.2%,　respectively.　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.