In　nature,　the　deep　forest　(DF)　algorithm　opened　the　deep　learning　model　of　non-neural　network　structure　firstly.　Due　to　the　characteristics　of　non-differential　form-based　learners　and　without　requiring　a　large　amount　of　training　data,　DF　has　become　an　important　direction　in　the　industry　and　academic　domain.　Thus,　the　existing　DF　algorithm　was　generalized　and　summarized,　in　which　its　main　structure　and　characteristics　were　reviewed.　First,　the　structure　and　properties　of　DF　were　introduced.　Further,　the　current　research　was　classed　into　five　research　directions,　i.e.,　introducing　feature　engineering,　improving　representation　learning,　modifying　base　learner,　modifying　the　hierarchical　structure,　and　introducing　weight　configuration,　which　were　analyzed　and　summarized,　respectively.　Then,　the　state　of　the　art　application　status　of　DF　algorithms　in　different　fields　was　introduced　and　the　challenges　and　future　research　direction　of　the　DF　algorithm　were　proposed.　Finally,　the　work　of　this　paper　was　summarized.　©　2022,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.