Recently,　model　stealing　attacks　are　widely　studied　but　most　of　them　are　focused　on　stealing　a　single　non-discrete　model,　e.g.,　neural　networks.　For　ensemble　models,　these　attacks　are　either　non-executable　or　suffer　from　intolerant　performance　degradation　due　to　the　complex　model　structure　(multiple　sub-models)　and　the　discreteness　possessed　by　the　sub-model　(e.g.,　decision　trees).　To　overcome　the　bottleneck,　this　paper　proposes　a　divide-and-conquer　strategy　called　DivTheft　to　formulate　the　model　stealing　attack　to　common　ensemble　models　by　combining　active　learning　(AL).　Specifically,　based　on　the　boosting　learning　concept,　we　divide　a　hard　ensemble　model　stealing　task　into　multiple　simpler　ones　about　single　sub-model　stealing.　Then,　we　adopt　AL　to　conquer　the　data-free　sub-model　stealing　task.　During　the　process,　the　current　AL　algorithm　easily　causes　the　stolen　model　to　be　biased　because　of　ignoring　the　past　useful　memories.　Thus,　DivTheft　involves　a　newly　designed　uncertainty　sampling　scheme　to　filter　reusable　samples　from　the　previously　used　ones.　Experiments　show　that　compared　with　the　prior　work,　DivTheft　can　save　almost　50%　queries　while　ensuring　a　competitive　agreement　rate　to　the　victim　model.