In　this　paper,　to　improve　the　efficiency　of　infrared　(IR)　ship　target　tracking,　an　efficient　SiamRPN++　method　based　on　AlexNet　with　cross　connection　and　spatial　transformer　network　is　proposed.　The　cross-connection　method　integrates　the　features　of　shallow　layers　and　the　deep　layers　to　increase　the　spatial　information　of　the　output　features.　To　reduce　the　influence　of　target　rotation　and　scaling　on　tracking　accuracy,　we　introduced　the　spatial　transformer　network　to　explicitly　learn　rotation　invariance,　which　can　supplement　the　implicit　rotation　invariance　learned　by　convolutional　neural　network.　Moreover,　in　order　to　train　and　evaluate　the　model　more　appropriately　and　to　deal　with　the　problem　of　the　lacking　IR　ship　video　target　tracking　data　set,　we　constructed　an　IR　ship　video　tracking　data　set　including　6725　frames　of　images.　The　experimental　results　show　that　the　proposed　method　can　effectively　improve　the　speed　to　63.9　FPS,　which　is　9.7　times　faster　than　the　SIAMRPN++　method　under　the　condition　of　ensuring　the　accuracy　and　the　average　intersection　of　union　(mIoU).　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.