This　paper　combines　machine　learning　models　and　heuristic　intelligent　optimization　algorithms　to　propose　an　intelligent　prediction　method　for　surface　settlement　curves　during　shield　tunnel　excavation.　First,　a　numerical　analysis　model　for　shield　tunnel　excavation　was　established,　taking　the　effects　of　equivalent　layer　elastic　modulus,　soil　elastic　modulus,　tunnel　radius,　soil　friction　angle,　and　cohesion　into　account.　A　database　of　1　680　surface　settlement　curves　under　different　working　conditions　was　constructed.　Then,　the　influence　of　geological　and　lining　mechanical　parameters,　as　well　as　tunnel　geometric　parameters,　on　the　surface　settlement　curves　was　analyzed.　The　Peck　function　was　used　to　fit　the　obtained　surface　settlement　trough　curves,　and　the　maximum　surface　settlements　and　settlement　trough　width　coefficients　under　corresponding　working　conditions　were　obtained.　Finally,　the　particle　swarm　optimization　algorithm　(PSO)　was　used　to　optimize　the　hyperparameters　or　random　numbers　of　four　machine　learning　methods,　namely　multi-layer　perceptron　(MLP),　extreme　learning　machine　(ELM),　random　forest　(RF),　and　support　vector　regression　(SVR).　Four　finite　element　simulation　proxy　models　for　shield　tunnel　excavation　were　established,　and　the　surface　settlement　curves　of　the　shield　tunnel　were　predicted.　The　prediction　results,　prediction　errors,　and　evaluation　indexes　of　the　models　were　compared　and　analyzed.　Results　indicate　that　the　PSO-SVR　model　performs　best　during　training　and　testing.　The　established　intelligent　prediction　method　for　surface　settlement　of　shield　tunnels　has　high　computational　accuracy　and　efficiency,　and　can　reasonably　and　efficiently　predict　the　distribution　pattern　of　surface　settlement　curves.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.