Cable　truss　structures　are　composed　of　edge　cables　and　internal　connection　members.　A　common　design　involves　internal　members　arranged　in　a　continuous　oblique　pattern,　which　is　efficiently　employed　in　long-span　space　structures.　This　configuration　primarily　governs　the　equilibrium　of　free　nodes　through　the　curve　of　the　edge　cables.　However,　manually　adjusting　the　prestress　to　align　with　the　edge　cable　shape　is　challenging　due　to　the　variable　skew　angles　of　the　internal　members.　To　address　this,　this　paper　proposes　a　deep　learn　method　to　establish　a　mapping　relationship　among　the　edge　cable　curve,　the　coordinates　of　the　free　nodes,　and　the　prestress　distribution.　The　force　density　distribution　is　automatically　adjusted　by　comparing　the　coordinates　of　the　free　nodes　with　the　edge　cable　shape.　Based　on　the　curve　characteristics　of　the　edge　cable,　the　polynomial　power　function　is　fitted　using　the　least　squares　method.　A　deep　neural　network　model　with　four　hidden　layers　and　Adam　optimizer,　using　constrained　node　coordinates　as　input　and　force　density　distribution　as　output,　successfully　achieved　the　rational　configuration　of　several　typical　diagonal　cable　truss　structures.　The　edge　cable　shape　of　each　cable　truss　can　be　controlled　by　automatically　adjusting　its　configuration.　This　method　is　suitable　for　cable　trusses　with　complex　space　topology　and　achieves　a　high　degree　of　automation.