Axis　deviation　during　shield　tunneling　may　cause　quality　and　safety　problems,　such　as　segment　dislocation　and　cracking.　Therefore,　the　shield　attitude　must　be　accurately　predicted　and　effectively　controlled.　An　intelligent　prediction　model　and　control　method　for　shield　attitude　based　on　machine　learning　are　proposed　in　this　study.　First,　a　shield　attitude　prediction　model　based　on　Bayesian　optimization　(BO)　and　support　vector　regression　(SVR)　is　established　using　monitored　construction　data.　The　nonlinear　relationship　among　construction　parameters,　stratum　information,　and　shield　attitude　is　revealed.　Second,　using　the　simulated　annealing(SA)　algorithm,　the　shield　attitude　control　method　for　the　dynamic　adjustment　of　controllable　construction　parameters　is　proposed　and　applied　in　the　engineering　practice　of　the　South　Park　station-Sancha　Street　station　section　of　the　Binhai　express　in　Fuzhou,　China.　Some　conclusions　are　drawn:　(1)　After　data　preprocessing,　feature　screening,　and　BO　hyperparameter　optimization,　the　SVR-based　shield　attitude　prediction　model　demonstrates　excellent　prediction　performance　and　generalization　ability.　(2)　It　is　important　to　set　some　optimization　rules　reasonably　within　the　SA　algorithm　to　ensure　the　operability　of　the　recommended　controllable　construction　parameters.　(3)　The　application　of　the　proposed　control　method　in　the　case　study　shows　that　the　vertical　deviation　of　the　shield　tail　is　reduced　from　45　mm　to　18　mm　during　the　successive　10-ring　tunneling　process,　realizing　a　continuous　and　stable　deviation　correction.　©　2024　Editorial　Office　of　Tunnel　Construction.　All　rights　reserved.