Autoencoder　is　an　unsupervised　learning　model,　which　can　automatically　learn　data　features　from　a　large　number　of　samples　and　can　act　as　a　dimensionality　reduction　method.　With　the　development　of　deep　learning　technology,　autoencoder　has　attracted　the　attention　of　many　scholars.　Researchers　have　proposed　several　improved　versions　of　autoencoder　based　on　different　application　fields.　First,　this　paper　explains　the　principle　of　a　conventional　autoencoder　and　investigates　the　primary　development　process　of　an　autoencoder.　Second,　We　proposed　a　taxonomy　of　autoencoders　according　to　their　structures　and　principles.　The　related　autoencoder　models　are　comprehensively　analyzed　and　discussed.　This　paper　introduces　the　application　progress　of　autoencoders　in　different　fields,　such　as　image　classification　and　natural　language　processing,　etc.　Finally,　the　shortcomings　of　the　current　autoencoder　algorithm　are　summarized,　and　prospected　for　its　future　development　directions　are　addressed.　©　2023　Elsevier　B.V.