Scene　classification　is　a　challenging　problem　in　computer　vision　applications　and　can　be　used　to　model　and　analyze　a　special　complex　system,　the　internet　community.　The　spatial　PACT　(Principal　component　Analysis　of　Census　Transform　histograms)　is　a　promising　representation　for　recognizing　instances　and　categories　of　scenes.　However,　since　the　original　spatial　PACT　only　simply　concatenates　compact　census　transform　histograms　at　all　levels　together,　all　levels　have　the　same　contribution,　which　ignores　the　difference　among　various　levels.　In　order　to　ameliorate　this　point,　we　propose　an　adaptive　multilevel　kernel　machine　method　for　scene　classification.　Firstly,　it　computes　a　set　of　basic　kernels　at　each　level.　Secondly,　an　effective　adaptive　weight　learning　scheme　is　employed　to　find　the　optimal　weights　for　best　fusing　all　these　base　kernels.　Finally,　support　vector　machine　with　the　optimal　kernel　is　used　for　scene　classification.　Experiments　on　two　popular　benchmark　datasets　demonstrate　that　the　proposed　adaptive　multilevel　kernel　machine　method　outperforms　the　original　spatial　PACT.　Moreover,　the　proposed　method　is　simple　and　easy　to　implement.