Advances in high-sensitivity detection technology have made single-photon-level sensitivity feasible. Combined with high-precision timing technology, laser lidar using time-dependent single-photon counting has emerged, offering substantial improvements in measurement resolution and accuracy. However, this high-sensitivity detection is highly vulnerable to environmental noise, generating considerable noise data during photon-counting lidar measurements. To address the influence of noise, a photon-counting lidar point cloud data denoising algorithm based on a backpropagation (BP) neural network is proposed. By selecting and normalizing the point cloud data feature values, the BP neural network is trained to accurately perform binary classification denoising. The proposed algorithm minimizes the human error associated with conventional denoising algorithms, delivers excellent denoising performance, and is strongly adaptable to various detection environments. Even under strong background noise detection conditions, the proposed algorithm obtains an F-number of 0.9773. In addition, the proposed algorithm exhibits good information extraction capabilities under simulated background noise conditions, and its consistency advantage is further confirmed through validation on ICESAT-2 point cloud experimental data.