Unmanned　aerial　vehicle　(UAV)-based　wireless　sensor　networks　(WSNs)　hold　great　promise　for　supporting　ground-based　sensors　due　to　the　mobility　of　UAVs　and　the　ease　of　establishing　line-of-sight　links.　UAV-based　WSNs　equipped　with　mobile　edge　computing　(MEC)　servers　effectively　mitigate　challenges　associated　with　long-distance　transmission　and　the　limited　coverage　of　edge　base　stations　(BSs),　emerging　as　a　powerful　paradigm　for　both　communication　and　computing　services.　Furthermore,　incorporating　simultaneously　transmitting　and　reflecting　reconfigurable　intelligent　surfaces　(STAR-RISs)　as　passive　relays　significantly　enhances　the　propagation　environment　and　service　quality　of　UAV-based　WSNs.　However,　most　existing　studies　place　STAR-RISs　in　fixed　positions,　ignoring　the　flexibility　of　STAR-RISs.　Some　other　studies　equip　UAVs　with　STAR-RISs,　and　UAVs　act　as　flight　carriers,　ignoring　the　computing　and　caching　capabilities　of　UAVs.　To　address　these　limitations,　we　propose　an　energy-efficient　aerial　STAR-RIS-aided　computing　offloading　and　content　caching　framework,　where　we　formulate　an　energy　consumption　minimization　problem　to　jointly　optimize　content　caching　decisions,　computing　offloading　decisions,　UAV　hovering　positions,　and　STAR-RIS　passive　beamforming.　Given　the　non-convex　nature　of　this　problem,　we　decompose　it　into　a　content　caching　decision　subproblem,　a　computing　offloading　decision　subproblem,　a　hovering　position　subproblem,　and　a　STAR-RIS　resource　allocation　subproblem.　We　propose　a　deep　reinforcement　learning　(DRL)-successive　convex　approximation　(SCA)　combined　algorithm　to　iteratively　achieve　near-optimal　solutions　with　low　complexity.　The　numerical　results　demonstrate　that　the　proposed　framework　effectively　utilizes　resources　in　UAV-based　WSNs　and　significantly　reduces　overall　system　energy　consumption.