In this paper, an infrared long-term target tracking method based on a state judgment module is proposed to address the problem of insufficient confidence in tracking in infrared target long-term tracking scenarios and tracking errors caused by misjudgment in similar targets. The Siamese network is employed as the base tracker. Geometric information, discriminative information, and state information output by the tracker are fused to build a state judgment module, including LSTM network stack adaptation design, scene adaptive adjustment design, and trigger re-detection mechanism design. It achieves accurate evaluation of infrared target tracking status and long-term tracking. The proposed method is tested and validated on the infrared long-term tracking dataset LSOTB-TIR-LT, and the results show that the F-score of the proposed method reaches 0.681, representing a 17.6% performance improvement compared with the classic Siam RPN++LT algorithm. It is particularly suitable for application scenarios where infrared targets are occluded, disappeared, interfered by similar targets, or undergo significant changes in scale for continuous tracking.