Accurate　prediction　of　shield　machine　position　and　attitude　is　crucial　for　ensuring　the　quality　of　tunnel　construction.　However,　current　machine　learning　models　for　predicting　the　position　and　attitude　deviations　of　shield　machines　encounter　significant　challenges　in　achieving　reliable　long-term　forecasting　during　shield　tunneling.　This　study　introduces　a　novel　deep　learning　model,　termed　1DCNN-Informer,　which　integrates　the　onedimensional　convolutional　neural　network　(1DCNN)　and　the　Informer　model.　The　model　was　trained　and　validated　using　datasets　from　the　Nanjing　Metro　shield　tunnel　project　in　China.　Furthermore,　the　1DCNN-Informer　model　was　transferred　to　datasets　from　both　similar　and　different　geological　conditions　using　the　domain　adversarial　neural　network　(DANN)　transfer　learning　method.　The　importance　of　input　features　was　analyzed　using　the　Shapley　additive　explanations　(SHAP)　method,　complemented　by　experiments　with　various　input　parameter　combinations.　Results　demonstrate　that　the　1DCNN-Informer　model　achieves　superior　performance　compared　to　the　Informer　model　and　surpasses　other　comparative　models,　such　as　PatchTST,　iTransformer,　and　Dlinear,　in　the　majority　of　input　sequence　length　and　prediction　sequence　length　combinations.　Additionally,　the　DANN　transfer　learning　method　significantly　enhances　the　1DCNN-Informer　model＇s　performance　in　the　target　domains　dataset.　The　cutterhead　rotation　speed,　advance　speed,　and　chamber　pressure　are　of　critical　importance　in　the　prediction　of　shield　position　and　attitude　deviation.　The　proposed　model　not　only　represents　a　significant　advancement　in　intelligent　shield　tunneling　but　also　holds　potential　for　broader　application　in　automated　equipment　operations　and　multi-domain　transfer　learning　studies　in　the　field　of　engineering.