In permafrost regions, freeze-thaw landslides (FTLs) are a typical geological hazard that poses significant threats to environments and infrastructure at local to regional scales. However, traditional visual interpretation and also new deep learning methods still have limitations in their ability to detect and recognize FTLs at high precision, especially for hidden and small FTLs. Here we propose a semi-automatic iterative recognition method that combines InSAR surface deformation, multi-source images, and topographic factors to achieve a more accurate FTLs dataset for the Qilian Mountain permafrost region. The methodology involves four key steps: (1) acquiring surface deformation data from SBAS-InSAR with a deformation rate threshold of ≥50 mm·a⁻¹; (2) statistically analyzing topographic factors based on an existing FTLs inventory to determine initial threshold ranges; (3) extracting overlapping mask regions of these factors; and (4) verifying FTL boundaries through visual interpretation of multi-source remote sensing images and iteratively optimizing the sample database until deformation rates stabilize. Results indicate that after five iterations, 98 new FTLs were identified, primarily consisting of hidden and small-scale FTLs. The method achieved a true positive rate of 93.3 %, indicating high accuracy. In addition, we found that areas with larger absolute values of deformation rate and higher seasonal deformations are more prone to FTLs. The application of this method demonstrates highly accurate and efficient FTL identification, providing a new technical approach for monitoring and assessing the FTLs.