In　the　Energy-Harvesting　(EH)　Cognitive　Internet　of　Things　(EH-CIoT)　network,　due　to　the　broadcast　nature　of　wireless　communication,　the　EH-CIoT　network　is　susceptible　to　jamming　attacks,　which　leads　to　a　serious　decrease　in　throughput.　Therefore,　this　paper　investigates　an　anti-jamming　resource-allocation　method,　aiming　to　maximize　the　Long-Term　Throughput　(LTT)　of　the　EH-CIoT　network.　Specifically,　the　resource-allocation　problem　is　modeled　as　a　Markov　Decision　Process　(MDP)　without　prior　knowledge.　On　this　basis,　this　paper　carefully　designs　a　two-dimensional　reward　function　that　includes　throughput　and　energy　rewards.　On　the　one　hand,　the　Agent　Base　Station　(ABS)　intuitively　evaluates　the　effectiveness　of　its　actions　through　throughput　rewards　to　maximize　the　LTT.　On　the　other　hand,　considering　the　EH　characteristics　and　battery　capacity　limitations,　this　paper　proposes　energy　rewards　to　guide　the　ABS　to　reasonably　allocate　channels　for　Secondary　Users　(SUs)　with　insufficient　power　to　harvest　more　energy　for　transmission,　which　can　indirectly　improve　the　LTT.　In　the　case　where　the　activity　states　of　Primary　Users　(PUs),　channel　information　and　the　jamming　strategies　of　the　jammer　are　not　available　in　advance,　this　paper　proposes　a　Linearly　Weighted　Deep　Deterministic　Policy　Gradient　(LWDDPG)　algorithm　to　maximize　the　LTT.　The　LWDDPG　is　extended　from　DDPG　to　adapt　to　the　design　of　the　two-dimensional　reward　function,　which　enables　the　ABS　to　reasonably　allocate　transmission　channels,　continuous　power　and　work　modes　to　the　SUs,　and　to　let　the　SUs　not　only　transmit　on　unjammed　channels,　but　also　harvest　more　RF　energy　to　supplement　the　battery　power.　Finally,　the　simulation　results　demonstrate　the　validity　and　superiority　of　the　proposed　method　compared　with　traditional　methods　under　multiple　jamming　attacks.　©　2024　by　the　authors.