To address the issues of high fitting difficulty and frequent underfitting of the temperature component in traditional dam displacement monitoring models when applied to high-altitude concrete dams. The effects of air temperature, radiation, reservoir water temperature, and insulation layers on the boundary temperature of the dam body were analyzed. Additionally, considering the lag effect of dam displacement caused by the transmission of external temperature within the dam body, a lag function was constructed using the Rayleigh distribution. The particle swarm optimization algorithm was employed to optimize the lag days, resulting in an improved displacement monitoring model for high-altitude concrete dams with improved temperature components. The improved model was validated using data from two monitoring points at different locations on a high-altitude concrete gravity dam. The results demonstrate that, compared to the traditional model, the improved model exhibits higher fitting accuracy and better predictive performance, making it suitable for application in high-altitude concrete dams.