Ecological　slope　protection　plays　a　crucial　role　in　increasing　slope　stability　and　preventing　landslide　disasters.　The　effectiveness　of　ecological　slope　protection　is　influenced　by　the　ecological　substrate　used.　In　this　study,　the　impact　of　materials　and　physical　properties　on　the　performance　of　ecological　substrates　based　on　red　soil　was　investigated.　Raw　materials　such　as　red　soil,　organic　fertilizer,　cement,　sawdust,　and　ferrous　sulfate　were　utilized　for　the　ecological　substrate,　with　Cynodon　dactylon　selected　as　the　slope-protection　plant.　Orthogonal　experiments　L25(53)　with　three　factors　and　five　levels　were　conducted　to　analyze　the　growth　status　of　Cynodon　dactylon,　shear　strength,　and　degree　of　disintegration　after　dry-wet　cycles.　The　study　examined　the　interrelationships　among　multiple　factors　for　ecological　substrates　using　Pearson　correlation　and　statistical　significance　tests.　Range　analysis　was　applied　to　determine　the　optimal　formula.　Furthermore,　an　ecological　substrate　neural　network　(ESNN)　model　based　on　a　variational　autoencoder　(VAE)　algorithm　was　initially　designed　to　verify　the　optimal　formula　of　ecological　substrates　and　predict　the　performance　of　experiment　arrays　generated　by　VAE.　The　research　findings　indicated　that　the　optimal　formula　obtained　through　ESNN　model　closely　aligned　with　that　obtained　by　range　analysis.　The　excellent　prediction　performance　of　ESNN　model　was　confirmed　by　the　field　experiments　with　random　level　values　assigned　to　three　factors.　Therefore,　the　ESNN　model　can　objectively　evaluate　orthogonal　experiments＇　performance　and　provide　property　predictions　for　plant　growth　and　slope　stability　for　on-site　construction　conditions.　This　research　provides　a　design　method　suitable　for　road　ecological　slope　protection　under　red　soil　conditions.