The work aims to propose a segmentation method of scattered electronic components based on edge extraction and adaptive region growth threshold of average normal angle and average point cloud density of point cloud cluster, so as to overcome the difficulty of dividing electronic components in counting of scattered electronic components. The original point cloud data were preprocessed by voxel processing, RANSAC algorithm and statistical outlier filtering algorithm to remove a large number of irrelevant point clouds. The European clustering algorithm was used to roughly segment the preprocessing results to obtain the point cloud cluster of electronic components. The point cloud cluster was used as the threshold setting unit to avoid the unreasonable threshold setting. In general, edge points had larger normal angle and fewer neighborhood points than non-edge points. A method was proposed to remove the edge points in point cloud cluster by adaptive constraints of average normal angle and average point cloud density. Finally, the point cloud cluster with the edge points removed was finely segmented, and the region growth threshold was adaptively selected according to the average normal angle of the point cloud cluster after fine segmentation. Thus, each electronic component was segmented from the point cloud cluster through the improved region growth algorithm. Experimental results proved that the segmentation accuracy of the method was over 97%, and it took about 345 ms to segment 10 targets. The proposed method has good accuracy and practicability, and has better segmentation effect than the traditional segmentation algorithm, which can accurately segment each electronic component from the complex scene.