In　this　paper,　we　concentrate　on　a　stochastic　non-monotone　DR-submodular　maximization　problem　over　a　convex　constraint　C,　where　the　objective　function　arises　as　an　expectation　of　a　set　of　stochastic　functions.　We　develop　an　algorithm　named　SPIDER-FW,　which　is　a　stochastic　version　of　the　classical　Frank-Wolfe　algorithm　with　1/4　(1-　min(x　epsilon　C)　parallel　to　x　parallel　to(infinity))F(x*)　-　epsilon　(in　expectation)　approximation　guarantee,　the　best　guarantee　so　far　for　the　above　setting,　achieved　with　O(1/epsilon)　iterations,　and　O(1/epsilon(2))　first-order　oracle　calls.