To　tackle　a　challenging　energy　efficiency　problem　caused　by　the　growing　mobile　Internet　traffic,　this　paper　proposes　a　deep　reinforcement　learning　(DRL)-based　green　content　task　offloading　scheme　in　cloud-edge-end　cooperation　networks.　Specifically,　we　formulate　the　problem　as　a　power　minimization　model,　where　requests　arriving　at　a　node　for　the　same　content　can　be　aggregated　in　its　queue　and　in-network　caching　is　widely　deployed　in　heterogeneous　environments.　A　novel　DRL　algorithm　is　designed　to　minimize　the　power　consumption　by　making　collaborative　caching　and　task　offloading　decisions　in　each　slot　on　the　basis　of　content　request　information　in　previous　slots　and　current　network　state.　Numerical　results　show　that　our　proposed　content　task　offloading　model　achieves　better　power　efficiency　than　the　existing　popular　counterparts　in　cloud-edge-end　collaboration　networks,　and　fast　converges　to　the　stable　state.