Reasonable　shield　tunnelling　parameters　play　a　crucial　role　in　controlling　ground　stability　and　enhancing　tunnelling　efficiency.　Predicting　shield　tunnelling　parameters　before　excavation　is　of　paramount　importance.　A　novel　deep　learning　method　is　introduced,　integrating　bidirectional　long　short-term　memory　(Bi-LSTM)　layers,　and　fully　connected　(FC)　layers　to　fuse　current　and　historical　data　for　shield　tunnelling　parameters　prediction.　Historical　data　captures　the　impact　of　excavated　sections　on　the　current　predicted　ring,　while　current　data　considers　present　conditions.　A　feature　fusion　method　eliminates　dimensional　differences　between　historical　and　current　data.　The　resulting　tensor,　encompassing　both　data　types,　is　fed　into　the　FC　layer　to　generate　predictions.　The　effectiveness　of　the　method　is　demonstrated　by　predicting　shield　cutter　head　torque　for　Qingdao　Metro　Line　4　in　China,　outperforming　traditional　Bi-LSTM,　MLP　and　RF　methods　significantly.　Ablation　studies　further　analyze　the　impact　of　different　component　modules　and　structural　parameters　on　model　performance.　Overall,　this　innovative　approach　offers　accurate　shield　tunnelling　parameters　prediction,　enhancing　ground　stability　and　tunnelling　efficiency.　©　2024　Elsevier　Ltd