The high-speed railroad on the Qinghai-Tibetan Plateau is located in the highest altitude region in the world, and the permafrost roadbed has been subjected to freeze-thaw cycles for a long time, which leads to disasters such as freezing and thawing of the roadbed, and threatens the safety of railroad operation. In this study, a risk prediction model of freezing and thawing disaster on the roadbed of the Qinghai-Tibet Plateau High-speed Railway is constructed to provide technical support for ensuring the safety of railroad operation in high-altitude areas. The study firstly clarifies the definition and classification of freeze-thaw disaster, which is divided into five types based on the freeze-thaw mechanism: cold weathering-gravity freeze-thaw disaster, freeze-thaw creep-gravity freeze-thaw disaster, freeze-thaw frost cracking disaster, thawing and sinking freeze-thaw disaster, and freezing and sliding disaster. By combining wavelet decomposition, ARIMA and BP neural network methods, a high-precision prediction model was established to assess the temperature change of roadbed and the risk of freeze-thaw hazard. The study collected 685 sets of data for model training and testing, and the results showed that the correlation coefficients R² of the training and testing data reached 0.98 and 0.97, respectively, and the percentage of error less than 1% accounted for 90% of the total data. The results applied to the Totuohe and Nagqu station areas on the Tibetan Plateau show that the predicted value of the roadbed surface thaw index in 2020 in the Totuohe area is 1715°C·d, the freezing index is -2024°C·d, and the thaw ratio is 0.8473, which is an increase of more than 7% compared with that in 2000; the predicted value of the thaw ratio in the Nagqu area reaches 1.7768, up more than 10% compared with 2000, indicating that the frozen soil of the roadbed in this region is developing toward thawing, and corresponding protective measures need to be taken to cope with the potential risk of freeze-thaw disaster.