This　study　applies　convolutional　neural　networks　(CNNs)　and　digital　morphogenesis　research　methods　to　perform　biomimetic　design　of　the　morphology　of　3D　printed　materials,　furthering　structural　innovation　based　on　the　lightweight　sustainability　of　biomimetic　materials.　Natural　two-dimensional　forms　such　as　leaf　veins,　spider　webs,　and　dragonfly　wings　are　selected　for　digital　reconstruction　into　three-dimensional　biomimetic　forms.　This　process　involves　transferring　the　material　properties　and　structural　advantages　of　natural　two-dimensional　biological　forms　to　three-dimensional　models.　Hence,　digital　methods　are　employed　to　create　three-dimensional　representations　of　leaf　veins,　spider　webs,　and　dragonfly　wings　while　preserving　their　structural　performance　advantages　observed　in　nature.　CNNs　style　transfer　technologies　are　utilized,　employing　53　cross-sectional　images　of　3D　models　as　content　images　for　the　style　transfer　algorithm,　alongside　natural　two-dimensional　form　images　as　style　images.　This　allows　for　the　parametric　reconstruction　of　three-dimensional　biomimetic　models.　Finally,　a　series　of　mechanical　and　material　performance　tests　are　conducted　to　validate　the　mechanical　and　structural　performance　of　3D　printed　biomimetic　structural　morphologies.　This　study　presents　a　research　methodology　for　the　digital　reconstruction　of　natural　two-dimensional　forms　into　three-dimensional　representations　and　innovatively　applies　digital　technologies　such　as　CNNs　to　material　morphology　research.　Through　the　application　of　digital　morphogenesis　research　methods,　this　study　explores　the　sustainability　and　innovation　of　3D　printed　materials.