Two-stage　submodular　maximization　problem　under　cardinality　constraint　has　been　widely　studied　in　machine　learning　and　combinatorial　optimization.　In　this　paper,　we　consider　knapsack　constraint.　In　this　problem,　we　give　n　articles　and　m　categories,　and　the　goal　is　to　select　a　subset　of　articles　that　can　maximize　the　function　F(S).　Function　F(S)　consists　of　m　monotone　submodular　functions　f(j),　j=1,2,　...,　m,　and　each　f(j)　measures　the　similarity　of　each　article　in　category　j.　We　present　a　constant-approximation　algorithm　for　this　problem.