The next-generation satellite LiDAR systems， including the Ice， Cloud， and land Elevation Satellite-2 （ICESat-2） and the Global Ecosystem Dynamics Investigation （GEDI）， offer unique advantages in estimating forest canopy height. The fusion of these two LiDAR datasets not only increases the sample size for canopy height retrieval but also allows for spatial complementarity between different datasets. First， the Random Forest-Recursive Feature Elimination （RF-RFE） method was used to select photon feature parameters. Subsequently， five fusion models—Stepwise Linear Regression （SLR）， Random Forest （RF）， Light Gradient Boosting Machine （LightGBM）， Random Forest with Stepwise Linear Regression （RF-SLR）， and Particle Swarm Optimization Random Forest （PSO-RF）—were analyzed for their applicability. The optimal model was selected to construct a point-scale canopy height dataset， which was then combined with multi-source remote sensing imagery to map the canopy height in Qilian Mountain National Park. Finally， the retrieval results were compared with existing canopy height products using GEDI footprint data and field survey data. The results showed that： （1） the Particle Swarm Optimization Random Forest （PSO-RF） model provided the best fusion performance （R2 = 0.71； RMSE = 3.15 m； MAE = 2.66 m）； （2） the retrieval model based on PSO-RF fusion of point-scale canopy height data achieved the highest accuracy （R2 = 0.56； RMSE = 3.02 m； MAE = 2.38 m）； （3） compared to existing canopy height products， the retrieval results demonstrated higher accuracy （based on GEDI footprint data： R2 = 0.43； RMSE = 4.50 m； MAE = 3.59 m）， and the errors were smaller when compared to field survey data （R2 = 0.36； RMSE = 3.15 m； MAE = 2.56 m）. The findings reflect the spatial distribution pattern of vegetation in Qilian Mountain National Park and provide scientific support for forest resource management， carbon sequestration estimation， and ecological resource conservation.