With　the　rapid　development　of　Internet　of　Things　technology　and　cloud　computing　technology,　Fog　Computing　emerges　with　the　demand　of　distributed　and　decentralized.　Blockchain　is　considered　to　be　an　advanced　technology,　which　enables　Fog　Computing　data　to　be　stored　and　shared　safely,　greatly　improving　the　security　and　privacy　of　Fog　Computing.　However,　the　Blockchain　technology　currently　applied　in　Fog　Computing　still　has　some　significant　disadvantages,　one　of　which　is　the　low　throughput　performance.　In　order　to　meet　the　demand　of　improving　throughput,　this　paper　proposes　a　Blockchain-Enabled　Fog　Computing　performance　optimization　framework　based　on　Deep　Reinforcement　Learning　(DRL),　which　can　dynamically　adjust　according　to　the　system　state　to　improve　throughput.　Dueling　Deep　Reinforcement　Learning　is　adopted　to　obtain　the　optimal　configuration,　which　can　be　dynamically　adjusted　according　to　Fog　node　status,　network　bandwidth　and　user＇s　quality　of　service　(QoS)　requirements,　including　dynamic　selection　of　block　producers,　block　size　and　network　bandwidth　allocation.　Simulation　results　show　that　the　proposed　scheme　can　effectively　improve　throughput　performance.　©　2022　ACM.