To　satisfy　the　requirements　of　content　distribution　in　computation-intensive　and　delay-sensitive　services,　this　paper　presents　a　novel　joint　task　offloading　and　content　caching　(JTOCC)　scheme　in　multi-cell　multi-carrier　non-orthogonal　multiple-access　(MCMC-NOMA)-assisted　cloud-edge-terminal　cooperation　networks.　Based　on　queuing　theory,　we　formulate　a　delay　minimization　model　that　aggregates　users＇　requests　to　reduce　repeated　content　delivery.　To　minimize　network　latency,　the　model　is　decomposed　into　three　subproblems:　task　offloading,　user　clustering　and　communication　resource　allocation,　and　cache　state　updating.　In　each　slot,　the　task　offloading　subproblem　is　solved　utilizing　deep　reinforcement　learning　(DRL)　under　a　resource-constrained　cloud-edge-terminal　setting.　During　a　transition　between　slots,　mobile　terminals　are　grouped　using　K-means-based　user　clustering,　and　the　allocations　of　the　subchannels　and　transmit　power　are　optimized　utilizing　matching　theory　and　successive　convex　approximation　(SCA),　respectively.　Contents　cached　at　the　network　nodes　are　updated,　according　to　long-short-term　memory　(LSTM)-based　predicted　popularity.　Simulations　show　that　the　proposed　JTOCC　model　achieves　lower-delay　content　distribution　than　its　existing　counterparts　in　cloud-edge-terminal　cooperation　environments,　and　converges　fast　in　heterogeneous　networks.