An　incremental　attribute　reduction　algorithm　for　complex　information　systems　with　discrete　and　continuous　mixed　attributes　was　proposed.　Firstly,　the　knowledge　granulation　in　the　granular　computing　model　was　extended　under　the　mixed　information　system,　the　neighborhood　knowledge　granulation　was　proposed,　and　a　non-incremental　attribute　reduction　algorithm　based　on　the　neighborhood　knowledge　granulation　was　constructed.　Then,　the　incremental　computation　of　neighborhood　knowledge　granulation　with　the　increase　of　objects　was　studied　under　the　mixed　information　system,　and　the　efficiency　of　the　computation　was　proved　theoretically.　Finally,　an　incremental　attribute　reduction　algorithm　for　mixed　information　systems　based　on　neighborhood　knowledge　granulation　was　proposed.　Experimental　results　of　UCI　datasets　show　that　the　proposed　algorithm　has　high　incremental　attribute　reduction　performance　in　mixed　information　systems.　©　2019,　Editorial　Department,　Journal　of　South　China　University　of　Technology.　All　right　reserved.