Fiber　Reinforced　Polymer　has　been　widely　used　in　the　retrofit　of　existing　structures　and　the　construction　of　new　structures.　The　ultimate　conditions　and　stress-strain　model　of　FRP-confined　composites　are　critical　to　structural　design　and　prediction　of　structural　response,　especially　under　extreme　loads　such　as　earthquakes.　In　this　paper,　a　data-driven　neural　network　prediction　model　for　ultimate　conditions　and　stress-strain　constitutive　relation　of　FRP-confined　concrete　is　proposed,　and　the　validity　and　accuracy　of　the　model　are　verified.　A　uniaxial　compression　database　containing　169　FRP-confined　normal　concrete　cylinders　is　collected　from　the　open　literature,　and　the　quality　of　the　database　is　examined　and　evaluated　in　detail.　Based　on　the　feed　forward　neural　network　technology,　a　prediction　model　for　the　ultimate　conditions　of　FRP-confined　normal　concrete　cylinders　is　established.　Configurations　and　hyper-parameters　of　the　network　are　carefully　analyzed,　and　the　optimal　model　is　used　for　prediction　and　comparison.　Besides,　a　uniaxial　stress-strain　model　for　FRP-confined　concrete　is　established　using　a　neural　network　with　a　recursive　structure.　The　prediction　accuracy　of　the　proposed　model　is　proven　to　be　superior　to　the　existing　design-oriented　models.　The　data-driven　neural　network　prediction　models　developed　in　this　paper　can　provide　a　rapid　prediction　and　design　for　FRP-confined　composites.