Topology　optimization　is　a　critical　tool　for　modern　structural　design,　yet　existing　methods　often　prioritize　single　objectives　(e.g.,　compliance　minimization)　and　suffer　from　prohibitive　computational　costs,　especially　in　multi-objective　scenarios.　To　address　these　limitations,　this　paper　introduces　a　novel　two-stage　multi-objective　topology　optimization　(MOTO)　method　that　uniquely　integrates　data-driven　learning　with　physics-informed　refinement,　and　both　stages　are　implemented　within　nearly　identical　network　frameworks,　ensuring　simplicity　and　consistency　in　execution.　Firstly,　a　MOTO　mathematical　model　based　on　the　constraint　programming　method　that　considers　competing　objectives　of　compliance,　stress　distribution,　and　material　usage　was　constructed.　Secondly,　a　novel　neural　network　that　incorporates　shifted　windows　attention　mechanism　and　lightweight　modules　was　developed　to　enhance　feature　extraction　while　maintaining　computational　efficiency.　Finally,　the　proposed　model　was　trained　in　two　stages:　In　Stage-1,　utilizing　adaptive　input　tensors,　the　network　predicts　near-optimal　geometries　across　variable　design　domains　(including　rectangular　and　L-shaped　configurations)　and　diverse　boundary　conditions　in　real　time,　requiring　only　1,650　samples　per　condition.　In　Stage-2,　the　near-optimal　structures　from　Stage-1　were　physically　optimized　to　achieve　optimal　performance.　Experimental　results　demonstrate　that　the　method＇s　capability　to　generate　high-accuracy,　computationally　efficient　solutions　with　robust　generalization　capabilities.　It　effectively　tackles　challenges　associated　with　multi-scale　design　domains　and　non-convex　geometries,　various　and　even　untrained　boundary　conditions　while　significantly　reducing　data　dependency,　a　critical　advancement　for　data-driven　topology　optimization.　The　novel　approach　offers　new　insights　for　multi-objective　structural　design　and　promotes　advancements　in　structural　design　practices.