With　feature　size　scaling　and　complexity　increase　of　circuit　designs,　hotspot　detection　has　become　a　significant　challenge　in　the　very-large-scale-integration　(VLSI)　industry.　Traditional　detection　methods,　such　as　pattern　matching　and　machine　learning,　have　been　made　a　remarkable　progress.　However,　the　performance　of　classifiers　relies　heavily　on　reference　layout　libraries,　leading　to　the　high　cost　of　lithography　simulation.　Querying　and　sampling　qualified　candidates　from　raw　datasets　make　active　learning-based　strategies　serve　as　an　effective　solution　in　this　field,　but　existing　relevant　studies　fail　to　take　sufficient　sampling　criteria　into　account.　In　this　paper,　embedded　in　pattern　sampling　and　hotspot　detection　framework,　an　entropy-based　batch　mode　sampling　strategy　is　proposed　in　terms　of　calibrated　model　uncertainty　and　data　diversity　to　handle　the　hotspot　detection　problem.　Redundant　patterns　can　be　effectively　avoided,　and　the　classifier　can　converge　with　high　celerity.　Experiment　results　show　that　our　method　outperforms　previous　works　in　both　ICCAD2012　and　ICCAD2016　Contest　benchmarks,　achieving　satisfactory　detection　accuracy　and　significantly　reduced　lithography　simulation　overhead.