Recently,　stair　environment　perception　has　attracted　considerable　attention　in　humanoid　robots.　In　complex　stair　environments,　there　are　some　problems,　such　as　incomplete　stairs,　obstacle　occlusion,　and　unequal　stair　heights,　resulting　in　relatively　low　accuracy　in　constructing　stairs.　To　solve　this　problem,　we　propose　a　balanced　iterative　reduction　and　cohesive　hierarchical　clustering　method　for　stair　environment　perception,　which　consists　of　two　main　parts:　a　point　cloud　preprocessing　module　and　a　stair　modeling　module.　Specifically,　given　a　fixed　resolution　point　cloud,　we　first　preprocess　and　initially　cluster　the　data　using　a　hierarchy-based　balanced　iterative　reduction　and　clustering　algorithm　to　reduce　point　cloud　noise　and　establish　contextual　links　between　point　clouds.　Then,　we　construct　the　graph　between　points　and　calculate　the　mean　square　error　between　individual　points　on　this　basis　to　efficiently　construct　the　stair　plane　and　effectively　improve　the　accuracy　of　the　3D　model　of　the　stair.　To　evaluate　the　effectiveness　and　efficiency　of　the　algorithm,　we　conduct　experiments　on　RGB-D　sensor　datasets　collected　from　different　scenes,　and　the　experimental　results　show　that　the　proposed　algorithm　has　a　more　satisfactory　performance　in　terms　of　modeling　accuracy　and　computation　time　for　the　stair　environment.　©　2023　IEEE.