The　dynamic　mechanism　of　joint　proactive　caching　and　cache　replacement,　which　involves　placing　content　items　close　to　cache-enabled　edge　devices　ahead　of　time　until　they　are　requested,　is　a　promising　technique　for　enhancing　traffic　offloading　and　relieving　heavy　network　loads.　However,　due　to　limited　edge　cache　capacity　and　wireless　transmission　resources,　accurately　predicting　users＇　future　requests　and　performing　dynamic　caching　is　crucial　to　effectively　utilizing　these　limited　resources.　This　article　investigates　joint　proactive　caching　and　cache　replacement　strategies　in　a　general　mobile-edge　computing　(MEC)　network　with　multiple　users　under　a　cloud-edge-device　collaboration　architecture.　The　joint　optimization　problem　is　formulated　as　a　Markov　decision　process　(MDP)　problem　with　an　infinite　range　of　average　network　load　costs,　aiming　to　reduce　network　load　traffic　while　efficiently　utilizing　the　limited　available　transport　resources.　To　address　this　issue,　we　design　an　attention-weighted　deep　deterministic　policy　gradient　(AWD2PG)　model,　which　uses　attention　weights　to　allocate　the　number　of　channels　from　server　to　user,　and　applies　deep　deterministic　policies　on　both　user　and　server　sides　for　Cache　decision-making,　so　as　to　achieve　the　purpose　of　reducing　network　traffic　load　and　improving　network　and　cache　resource　utilization.　We　verify　the　convergence　of　the　corresponding　algorithms　and　demonstrate　the　effectiveness　of　the　proposed　AWD2PG　strategy　and　benchmark　in　reducing　network　load　and　improving　hit　rate.