To　solve　the　problem　of　blind　spot　edge　user　offloading　tasks　where　direct　communication　links　cannot　be　established　with　edge　servers,　an　intelligent　reflecting　surface　(IRS)-assisted　non-orthogonal　multiple　access　(NOMA)　communication　resource　allocation　optimization　algorithm　based　on　deep　reinforcement　learning　(DRL)　was　designed.　The　algorithm　aimed　to　obtain　the　maximum　system　benefit　weighted　by　system　sum　rate　and　energy　efficiency　(EE)　for　green　and　efficient　communication.　The　deep　deterministic　policy　gradient　(DDPG)　algorithm　was　adopted　to　jointly　optimize　the　power　allocation　and　phase-shift　matrix.　The　simulation　results　show　that　DDPG　algorithm　is　superior　to　other　comparative　experimental　algorithms　in　dealing　with　the　communication　resource　allocation　of　mobile　edge　computing　(MEC).　©　2024　Beijing　University　of　Technology.　All　rights　reserved.