Intelligent　prediction　of　reasonable　cutterhead　torque　can　effectively　reduce　the　stratum　disturbance　caused　by　shield　tunneling　and　reduce　cutterhead　wear,　which　is　of　great　significance　for　the　safe　and　efficient　construction　of　shield　tunnels.　A　time-series　intelligent　prediction　method　for　shield　tunnel　cutterhead　torque　is　proposed　based　on　intelligent　optimization　algorithms　and　deep-learning　neural　networks.　Bi-LSTM　neural　network　is　used　as　the　deep　learning　prediction　model.　The　particle　swarm　optimization　algorithm　(PSO),　genetic　optimization　algorithm　(GA),　sparrow　search　algorithm　(SSA),　and　hunger　game　search　algorithm　(HGS)　are　jointly　used　to　intelligently　determine　the　three　hyperparameters　of　the　Bi-LSTM　neural　network,　respectively,　namely　that,　learning　rate,　the　number　of　neurons,　and　dropout　rate.　Furthermore,　an　intelligent　prediction　method　for　the　cutterhead　torque　of　the　shield　tunnel　is　obtained.　The　root　mean　square　error　between　the　predicted　values　of　intelligent　models　and　measured　values　is　used　as　fitness　functions　for　four　optimization　algorithms.　Normalization　method　is　used　for　data　preprocessing　to　reduce　the　impact　of　magnitude　differences　between　different　parameters.　The　input　of　the　intelligent　prediction　model　considers　the　influence　of　15　parameters　on　the　cutterhead　torque,　including　1　tunnel　geometric　parameter,　6　geological　mechanical　parameters,　and　8　shield　tunneling　parameters.　The　performances　of　four　optimization　algorithms　for　determining　hyperparameters　of　Bi-LSTM　neural　network　are　evaluated　from　three　perspectives:　accuracy,　efficiency,　and　stability,　based　on　on-site　measured　data　of　shield　tunneling.　The　results　indicate　that　compared　with　the　other　three　optimization　algorithms,　the　HGS　method　has　better　stability,　faster　efficiency,　and　higher　computational　accuracy,　which　is　an　appropriate　method　to　determine　the　hyperparameters　of　Bi-LSTM.　©　2025　Science　Press.　All　rights　reserved.