Object　Constraint　Language　(OCL)　is　one　kind　of　lightweight　formal　specification,　which　is　widely　used　for　software　verification　and　validation　in　NASA　and　Object　Management　Group　projects.　Although　OCL　provides　a　simple　expressive　syntax,　it　is　hard　for　the　developers　to　write　correctly　due　to　lacking　knowledge　of　the　mathematical　foundations　of　the　first-order　logic,　which　is　approximately　half　accurate　at　the　first　stage　of　development.　A　deep　natural　network　named　DeepOCL　is　proposed,　which　takes　the　unrestricted　natural　language　as　inputs　and　automatically　outputs　the　best-scored　OCL　candidates　without　requiring　a　domain　conceptual　model　that　is　compulsively　required　in　existing　rule-based　generation　approaches.　To　demonstrate　the　validity　of　our　proposed　approach,　ablation　experiments　were　conducted　on　a　new　sentence-aligned　dataset　named　OCLPairs.　The　experiments　show　that　the　proposed　DeepOCL　can　achieve　state　of　the　art　for　OCL　statement　generation,　scored　74.30　on　BLEU,　and　greatly　outperformed　experienced　developers　by　35.19%.　The　proposed　approach　is　the　first　deep　learning　approach　to　generate　the　OCL　expression　from　the　natural　language.　It　can　be　further　developed　as　a　CASE　tool　for　the　software　industry.