With　the　increasing　complexity　of　applications,　many　delay-sensitive　and　compute-intensive　services　have　posed　significant　challenges　to　mobile　devices.　Addressing　how　to　efficiently　allocate　heterogeneous　network　resources　to　meet　the　computing　and　delay　requirements　of　terminal　services　is　a　pressing　issue.　In　this　paper,　a　new　cooperative　twin　delayed　deep　deterministic　policy　gradient　and　deep-Q　network　(TD3-DQN)　algorithm　is　introduced　to　minimize　system　latency　by　optimizing　computational　offloading　and　caching　placement　asynchronously.　Specifically,　the　task-partitioning　technique　divides　computing　tasks　into　multiple　subtasks,　reducing　the　response　latency.　A　DQN　intelligent　algorithm　is　presented　to　optimize　the　offloading　path　to　edge　servers　by　perceiving　network　resource　status.　Furthermore,　a　TD3　approach　is　designed　to　optimize　the　cached　content　in　the　edge　servers,　ensuring　dynamic　popularity　content　requirements　are　met　without　excessive　offload　decisions.　The　simulation　results　demonstrate　that　the　proposed　model　achieves　lower　latency　and　quicker　convergence　in　asymmetrical　cloud-edge　collaborative　networks　compared　to　other　benchmark　algorithms.