High-dimensional　features　are　extensively　accessible　in　machine　learning　and　computer　vision　areas.　How　to　learn　an　efficient　feature　representation　for　specific　learning　tasks　is　invariably　a　crucial　issue.　Due　to　the　absence　of　class　label　information,　unsupervised　feature　representation　is　exceedingly　challenging.　In　the　last　decade,　deep　learning　has　captured　growing　attention　from　researchers　in　a　broad　range　of　areas.　Most　of　the　deep　learning　methods　are　supervised,　which　is　required　to　be　fed　with　a　large　amount　of　accurately　labeled　data　points.　Nevertheless,　acquiring　sufficient　accurately　labeled　data　is　unaffordable　in　numerous　real-world　applications,　which　is　suggestive　of　the　needs　of　unsupervised　learning.　Toward　this　end,　quite　a　few　unsupervised　feature　representation　approaches　based　on　deep　learning　have　been　proposed　in　recent　years.　In　this　paper,　we　attempt　to　provide　a　comprehensive　overview　of　unsupervised　deep　learning　methods　and　compare　their　performances　in　text　categorization.　Our　survey　starts　with　the　autoencoder　and　its　representative　variants,　including　sparse　autoencoder,　stacked　autoencoder,　contractive　autoencoder,　denoising　autoencoder,　variational　autoencoder,　graph　autoencoder,　convolutional　autoencoder,　adversarial　autoencoder,　and　residual　autoencoder.　Aside　from　autoencoders,　deconvolutional　networks,　restricted　Boltzmann　machines,　and　deep　belief　nets　are　introduced.　Then,　the　reviewed　unsupervised　feature　representation　methods　are　compared　in　terms　of　text　clustering.　Extensive　experiments　in　eight　publicly　available　data　sets　of　text　documents　are　conducted　to　provide　a　fair　test　bed　for　the　compared　methods.