In　this　paper,　an　underlay　cognitive　radio　network　with　energy　harvesting　is　considered　which　works　in　slotted　mode.　SU　decides　to　transmit　data　or　harvest　energy　from　environment　according　to　the　available　energy　of　the　battery　and　the　channel　state　in　each　slot.　Considering　the　highly　dynamic　characteristics　of　channel　occupancy,　channel　gain　and　energy　arrival,　a　deep　Q-network　(DQN)　algorithm　in　deep　reinforcement　learning　is　proposed.　By　setting　the　corresponding　reward　function　under　different　channel　occupancy　states,　the　agent　can　select　the　appropriate　working　mode　and　value　of　transmit　power　according　to　the　change　of　cognitive　radio　environment.　After　a　period　of　learning,　agent　obtains　the　optimal　strategy　in　finite　time　slots.　Simulation　results　show　that　the　proposed　method　can　converge　and　perform　better　than　other　baseline　strategies.　©　2021　IEEE.