The　limited　availability　of　thermal　infrared　(TIR)　training　samples　leads　to　suboptimal　target　representation　by　convolutional　feature　extraction　networks,　which　adversely　impacts　the　accuracy　of　TIR　target　tracking　methods.　To　address　this　issue,　we　propose　an　unsupervised　cross-domain　model　(UCDT)　for　TIR　tracking.　Our　approach　leverages　labeled　training　samples　from　the　RGB　domain　(source　domain)　to　train　a　general　feature　extraction　network.　We　then　employ　a　cross-domain　model　to　adapt　this　network　for　effective　target　feature　extraction　in　the　TIR　domain　(target　domain).　This　cross-domain　strategy　addresses　the　challenge　of　limited　TIR　training　samples　effectively.　Additionally,　we　utilize　an　unsupervised　learning　technique　to　generate　pseudo-labels　for　unlabeled　training　samples　in　the　source　domain,　which　helps　overcome　the　limitations　imposed　by　the　scarcity　of　annotated　training　data.　Extensive　experiments　demonstrate　that　our　UCDT　tracking　method　outperforms　existing　tracking　approaches　on　the　PTB-TIR　and　LSOTB-TIR　benchmarks.