FRP　(Fiber　Reinforced　Polymer)　has　been　widely　used　in　the　reinforcement　and　renovation　of　existing　concrete　structures　and　new　structures.　FRP-constrained　concrete　columns　are　usually　subjected　to　reciprocal　cyclic　action　of　axial　pressure　under　seismic　action,　and　it　is　important　to　study　the　stress-strain　characteristics　of　FRP-constrained　concrete　under　cyclic　axial　pressure　for　the　application　of　FRP　in　practical　engineering.　In　this　paper,　a　neural　network　prediction　model　is　proposed　for　modeling　the　stress-strain　properties　of　FRP-confined　concrete　columns　under　cyclic　axial　pressure.　The　model　uses　long　short-term　memory　(LSTM)　units　to　model　the　hysteresis　behaviors　of　cyclic　stress-strain　curves,　and　the　physical　parameters　of　the　members　are　effectively　integrated　into　the　inputs　of　the　network.　The　model　can　be　efficiently　trained　in　an　end-to-end　manner　and　does　not　rely　on　any　expert　experience.　A　cyclic　axial　pressure　database　containing　166　FRP-constrained　plain　concrete　columns　was　produced,　by　means　of　which　the　accuracy　and　robustness　of　the　model　were　fully　evaluated.　The　results　show　that　the　average　prediction　error　of　the　test　set　is　only　0.32　MPa.　In　addition,　the　effects　of　network　structure　and　hyperparameters　were　discussed　in　detail,　and　the　results　show　that　the　model　has　excellent　predictive　performance.　©　2024　Tsinghua　University.　All　rights　reserved.