To　achieve　efficient　structural　design,　it　is　crucial　to　reduce　the　cost　of　materials　while　ensuring　structural　safety.　This　study　proposes　an　optimization　method　for　design　parameters　(DPs)　in　a　prestressed　steel　structure　driven　by　multi-factor　coupling.　To　accomplish　this,　a　numerical　proxy　model　of　prestressed　steel　structures　is　established　with　integration　of　DPs　and　mechanical　parameters　(MPs).　A　data　association-parameter　analysis-optimization　selection　system　is　established.　A　correlation　between　DPs　and　MPs　is　established　using　a　back　propagation　(BP)　neural　network.　This　correlation　provides　samples　for　parameter　analysis　and　optimization　selection.　MPs　are　used　to　characterize　the　safety　of　the　structure.　Based　on　the　safety　grade　analysis,　the　key　DPs　that　affect　the　mechanical　properties　of　the　structure　are　obtained.　A　mapping　function　is　created　to　match　the　MPs　and　the　key　DPs.　The　optimal　structural　DPs　are　obtained　by　setting　structural　materials　as　the　optimization　objective　and　safety　energy　as　the　constraint　condition.　The　theoretical　model　is　applied　to　an　80-m-span　gymnasium　and　verified　with　a　scale　test　physical　model.　The　MPs　obtained　using　the　proposed　method　are　in　good　agreement　with　the　experimental　results.　Compared　with　the　traditional　design　method,　the　design　cycle　can　be　shortened　by　more　than　90%.　Driven　by　the　optimal　selection　method,　a　saving　of　more　than　20%　can　be　achieved　through　reduction　of　structural　material　quantities.　Moreover,　the　cross-sectional　dimensions　of　radial　cables　have　a　substantial　influence　on　vertical　displacement.　The　initial　tension　and　cross-sectional　size　of　the　upper　radial　cable　exhibit　the　most　pronounced　impact　on　the　stress　distribution　in　that　cable.　The　initial　tension　and　cross-sectional　size　of　the　lower　radial　cable　hold　the　greatest　sway　over　the　stress　distribution　in　that　cable.