Caching　popular　files　at　the　edge　of　the　network　near　the　users　can　effectively　mitigate　the　pressure　on　the　backhaul　link　and　reduce　the　user＇s　transmission　delay.　The　effectiveness　of　content　caching　highly　depends　on　the　hit　rate　of　the　request,　so　the　research　of　content　caching　strategy　is　an　important　issue.　Inspired　by　reinforcement　learning　to　solve　complex　control　problems,　this　paper　studies　the　content　caching　problem　that　maximizing　the　use　of　the　cache　capacity　of　the　base　stations　to　minimize　the　average　transmission　delay　of　the　user　request　and　the　energy　loss　of　the　network.　Because　the　state　of　the　network　is　unpredictable　and　its　dimension　is　huge,　the　DDPG　(Deep　Deterministic　Policy　Gradient)　algorithm　is　used　to　interact　with　the　environment　to　learn　the　optimal　cache　strategy.　By　separating　the　action　function　network　and　the　value　function　network,　it　is　possible　to　optimize　each　other　in　the　iterative　process.　Our　results　show　that　the　proposed　framework　can　improve　cache　hit　ratio　and　reduce　network　energy　loss.