Context:　Code　readability,　which　correlates　strongly　with　software　quality,　plays　a　critical　role　in　software　maintenance　and　evolvement.　Although　existing　deep　learning-based　code　readability　models　have　reached　a　rather　high　classification　accuracy,　only　structural　features　are　utilized　which　inevitably　limits　their　model　performance.　Objective:　To　address　this　problem,　we　propose　to　extract　readability-related　features　from　visual,　semantic,　and　structural　aspects　from　source　code　in　an　attempt　to　further　improve　code　readability　classification.　Method:　First,　we　convert　a　code　snippet　into　a　RGB　matrix　(for　visual　feature　extraction),　a　token　sequence　(for　semantic　feature　extraction)　and　a　character　matrix　(for　structural　feature　extraction).　Then,　we　input　them　into　a　hybrid　neural　network　that　is　composed　of　BERT,　CNN,　and　BiLSTM　for　feature　extraction.　Finally,　the　extracted　features　are　concatenated　and　input　into　a　classifier　to　make　a　code　readability　classification.　Result:　A　series　of　experiments　are　conducted　to　evaluate　our　method.　The　results　show　that　the　average　accuracy　could　reach　85.3%,　which　outperforms　all　existing　models.　Conclusion:　As　an　innovative　work　of　extracting　readability-related　features　automatically　from　visual,　semantic,　and　structural　aspects,　our　method　is　proved　to　be　effective　for　the　task　of　code　readability　classification.　©　2022　Elsevier　Inc.