Abstract: The angle of repose and particle size distribution of bulk materials are core parameters for ensuring the safety and efficiency of continuous transportation in belt conveyors. To address the difficulties in point cloud segmentation caused by the complex trough shape and sag deformation of conveyor belts, as well as the reduced measurement accuracy due to particle occlusion and adhesion, this paper proposes a measurement method for the angle of repose and particle size distribution of bulk materials based on point cloud segmentation. To achieve high-fidelity segmentation of bulk materials, a bicubic polynomial surface model is constructed to adaptively fit the actual deformed surface of the conveyor belt using the weighted least squares method, thereby accurately extracting the pure point cloud of the bulk materials. On this basis, equidistant point cloud slicing combined with the Hough transform is employed to extract contour line features, which eliminates the interference from local large particle protrusions and enables accurate measurement of the local dynamic angle of repose. Simultaneously, a 3D depth field simulating the stacking morphology is constructed, and a 3D watershed algorithm is applied to accomplish the segmentation of bulk particles and calculate the median particle size. Experimental results demonstrate that the mean Intersection over Union (mIoU) of the proposed adaptive surface fitting segmentation algorithm reaches 95.84%. The absolute error of the local dynamic angle of repose measurement is controlled within ±1.35°, with a mean absolute error of only 0.66°. Furthermore, the particle size distribution measurement effectively overcomes the statistical bias caused by particle stacking and occlusion, and the corrected median particle size accurately falls within the benchmark interval of the standard mechanical sieving method. The proposed method significantly improves the measurement accuracy and robustness of bulk material state parameters under complex working conditions, providing reliable technical support for the intelligent monitoring of bulk material conveying systems.