Thermal　infrared　(TIR)　target　tracking　is　an　important　topic　in　the　computer　vision　area.　The　TIR　images　are　not　affected　by　ambient　light　and　have　strong　environmental　adaptability,　making　them　widely　used　in　battlefield　perception,　video　surveillance,　and　assisted　driving.　However,　TIR　target　tracking　faces　problems　such　as　relatively　insufficient　information　and　lack　of　target　texture　information,　which　significantly　affects　the　tracking　accuracy　of　the　TIR　tracking　methods.　To　solve　the　above　problems,　we　propose　a　TIR　target　tracking　method　based　on　a　Siamese　network　with　a　hierarchical　attention　mechanism　(called:　SiamHAN).　Specifically,　the　CIoU　Loss　is　introduced　to　make　full　use　of　the　regression　box　information　to　calculate　the　loss　function　more　accurately.　The　global　context　network　(GCNet)　attention　mechanism　is　introduced　to　reconstruct　the　feature　extraction　structure　of　fine-grained　information　for　the　fine-grained　information　of　TIR　images.　Meanwhile,　for　the　feature　information　of　the　hierarchical　backbone　network　of　the　Siamese　network,　the　ECANet　attention　mechanism　is　used　for　hierarchical　feature　fusion,　so　that　it　can　fully　utilize　the　feature　information　of　the　multilayer　backbone　network　to　represent　the　target.　On　the　LSOTB-TIR,　the　hierarchical　attention　Siamese　network　achieved　a　2.9%　increase　in　success　rate　and　a　4.3%　increase　in　precision　relative　to　the　baseline　tracker.　Experiments　show　that　the　proposed　SiamHAN　method　has　achieved　competitive　tracking　results　on　the　TIR　testing　datasets.