The　rapid　expansion　of　multi-cloud　environments　enables　the　fulfillment　of　the　dynamic　and　diverse　resource　requirements　of　cloud　applications.　Cumulative　data　processing　(CDP)　applications,　which　handle　incrementally　generated　data　in　stages　like　preprocessing　and　aggregate　analysis,　particularly　benefit　from　these　environments.　However,　existing　cloud　scheduling　solutions　struggle　to　handle　the　dynamic　accumulation　of　processed　data　and　the　long-term　data　operation　dependencies　in　CDP　applications.　Aiming　at　this　issue,　we　propose　a　novel　job　execution　model,　CDP-EM,　and　a　tailored　job　scheduling　strategy,　CDP-JS,　to　optimize　the　scheduling　of　CDP　applications　in　multi-cloud　environments.　The　CDP-EM　model　enables　dynamic　job　generation　and　dependency-aware　execution　for　CDP　applications,　while　the　CDP-JS　strategy　formulates　the　job　scheduling　problem　as　a　Markov　Decision　Process　(MDP),　utilizing　deep　reinforcement　learning　with　Proximal　Policy　Optimization　(PPO)　to　optimize　scheduling　decisions.　The　simulation　results　show　that　integrating　CDP-EM　and　CDP-JS　reduces　the　SLA　violation　rate　and　resource　cost　of　CDP　applications　by　an　average　of　34.8%　and　23.4%,　respectively.　Real-world　evaluations　show　average　reductions　of　27.2%　and　31.3%,　respectively.