In the process of airborne LiDAR point cloud cable line extraction, there are problems such as complex shape of the pole tower and high noise influence, which lead to low accuracy of cable line point cloud extraction. This paper proposes a cable line point cloud extraction and reconstruction method based on point cloud chunking processing, improved multidimensional filtering, and density clustering algorithm. Firstly, the point cloud filtering data processing technology, and its three key techniques of streamlining, filtering, and alignment in point cloud data preprocessing are introduced. Secondly, the overall point cloud is processed in chunks according to the direction of power lines. Then, on the basis of surface fitting algorithm, the idea of grid division is introduced to propose an improved multidimensional filtering algorithm with point cloud filtering. Finally, the cable line point cloud is accurately extracted by the given adaptive density clustering solution, and the method of this paper is tested and evaluated for accuracy based on the measured point cloud data. The results show that: using the algorithm to extract the cable line points of the integrated integrity rate of 95.9796% or more, a time can be realized in the successful extraction of the power line, in order to ensure the accuracy of the extraction at the same time to improve the extraction efficiency, the research in this paper can be for the intelligent inspection of the cable line to provide a good value of engineering applications.