For the problem that the traditional algorithm for the extraction of power tower is sensitive to ground fluctuations, and some points of ground are hard to exclude from the extraction result, a lightweight neural network was proposed with a direct input of the original point cloud data to implement the extraction of power tower. By dividing the original point cloud data into a number of voxel grids of equal size, the feature learning network and a convolutional neural network were used to extract the spatial and structural information in the voxel grid. Then the characteristics of relative height and point density which can be found in traditional algorithms were added to the feature to determine whether the voxel is a type of tower point cloud or not. Finally, clustering was used to eliminate isolated voxels to improve the accuracy and obtain the laser point cloud data of power towers. The experimental results show that the algorithm has an accuracy of about 95% for different terrains and interferences in the extraction of power towers. The algorithm can effectively extract the point cloud of power towers, and has a certain improvement in stability and accuracy compared with the method using grid or other methods, and also has a good anti-interference effect on other factors such as the existence of tall trees or vertical occlusion.