<p>Rockfall, as a frequent and sudden geohazard in high-steep slope areas, seriously threatens the safety of life and property. To effectively prevent and mitigate disasters, conducting rockfall investigations is crucial. This study takes the unstable rock mass on the left bank of the Longmen Yellow River Bridge as the object, aiming to improve the survey accuracy of unstable rock masses and the reliability of rockfall movement prediction. The study comprehensively applies field investigation and UAV nap-of-the-object photogrammetry technology, constructs a high-precision three-dimensional terrain model of the target high-steep slope, and accurately identifies the spatial distribution characteristics of joints and fractures in the unstable rock mass; on this basis, the main failure modes and stability conditions of the unstable rock mass are clarified through kinematic analysis. Further, using key parameters obtained from fracture system characterization, a representative discrete fracture network (DFN) model is constructed; this model is used to improve rockfall simulation, significantly enhancing the accuracy of volume estimation for potentially unstable blocks. The rockfall simulation results reveal the detailed kinematic characteristics of rockfall movement in the study area, providing key parameter support for subsequent protective engineering design. The study shows that the rockfall simulation method integrating UAV nap-of-the-object photogrammetry and DFN modeling technology effectively compensates for the blind spots of traditional investigation methods, significantly improves the accuracy and efficiency of unstable rock mass surveys and rockfall trajectory analysis on high-steep slopes, provides a new idea for risk assessment of unstable rock mass hazards and optimization of prevention and control engineering, and has positive disaster reduction application value.</p>