In　this　paper,　we　consider　a　problem　of　maximizing　regularized　submodular　functions　with　a　k-cardinality　constraint　under　streaming　fashion.　In　the　model,　the　utility　function　f(.)　=　g(.)-l(.)　is　expressed　as　the　difference　between　a　non-negative　monotone　non-decreasing　submodular　function　g　and　a　non-negative　modular　function　l.　In　addition,　the　elements　are　revealed　in　a　streaming　setting,　that　is　to　say,　an　element　is　visited　in　one　time　slot.　The　problem　asks　to　find　a　subset　of　size　at　most　k　such　that　the　regularized　utility　value　is　maximized.　Most　of　the　existing　algorithms　for　the　submodular　maximization　heavily　rely　on　the　non-negativity　assumption　of　the　utility　function,　which　may　not　be　applicable　for　our　regularized　scenario.　Indeed,　determining　if　the　maximum　is　positive　or　not　is　NP-hard,　which　implies　that　no　multiplicative　factor　approximation　is　existed　for　this　problem.　To　circumvent　this　challenge,　several　works　paid　attention　to　more　meaningful　guarantees　by　introducing　a　slightly　weaker　notion　of　approximation,　and　any　developed　algorithm　is　aim　to　construct　a　solution　S　satisfying　f(S)　＞=　rho　.　g(OPT)　-　l(OPT)　for　some　rho　＞　0.　In　this　work,　assume　there　is　a　weak.-approximation　for　the　k-cardinality　constrained　regularized　submodular　maximization,　we　develop　Distorted-Threshold-Streaming,　a　multi-pass　bicriteria　algorithm　for　the　streaming　regularized　submodular　maximization　with　the　k-cardinality　constraint　(SRSMCC),　which　produces　a　(rho/lambda,　1/lambda)-bicriteria　approximation　bymaking　over　O(log(lambda/rho)/epsilon)　passes,　consuming　O(k)　memory　and　using　O(log(lambda/rho)/epsilon)　queries　per　element,　where　lambda　=　2-(2　rho+2)/(3+root　5-4　rho)/(3+root　5-4　rho)/(2　rho+2)-1　and　any　accuracy　parameter　epsilon　＞　0.