The　paper　studies　the　secrecy　communication　threatened　by　a　single　eavesdropper　in　Energy　Harvesting　(EH)-based　cognitive　radio　networks,　where　both　the　Secure　User　(SU)　and　the　jammer　harvest,　store,　and　utilize　RF　energy　from　the　Primary　Transmitter　(PT).　Our　main　goal　is　to　optimize　the　time　slots　for　energy　harvesting　and　wireless　communication　for　both　the　secure　user　as　well　as　the　jammer　to　maximize　the　long-term　performance　of　secrecy　communication.　A　multi-agent　Deep　Reinforcement　Learning　(DRL)　method　is　proposed　for　solving　the　optimization　of　resource　allocation　and　performance.　Specifically,　each　sub-channel　from　the　Secure　Transmitter　(ST)　to　the　Secure　Receiver　(SR)　link,　along　with　the　jammer　to　the　eavesdropper　link,　is　regarded　as　an　agent,　which　is　responsible　for　exploring　optimal　power　allocation　strategy　while　a　time　allocation　network　is　established　to　obtain　optimal　EH　time　allocation　strategy.　Every　agent　dynamically　interacts　with　the　wireless　communication　environment.　Simulation　results　demonstrate　that　the　proposed　DRL-based　resource　allocation　method　outperforms　the　existing　schemes　in　terms　of　secrecy　rate,　convergence　speed,　and　the　average　number　of　transition　steps.