The original point cloud obtained directly by equipment such as laser scanners is usually affected by noise, which will affect subsequent processing, such as three-dimensional reconstruction and semantic segmentation; as a result, the point cloud denoising algorithm becomes particularly crucial. The majority of currently available point cloud denoising networks use the distance between noise and clean points as the objective function during iterative training, which may cause point cloud aggravation and outliers. To address the above issues, a new denoising network called multiscale score point (MSPoint) based on multiscale point cloud distribution fraction (i.e., the gradient of point-cloud logarithmic probability function) is proposed. The displacement prediction and feature extraction modules make up the majority of the MSPoint network. Input the neighborhood of the point cloud in the feature extraction module and strengthen the antinoise performance of MSPoint by adding multiscale noise disturbance to the data, thereby leading the extracted features to have a stronger expression ability. According to the fraction predicted by the fraction estimation unit, the displacement prediction module iteratively learns the displacement of noise points. MSPoint provides stronger robustness than previous approaches and a superior denoising impact, according to experimental results on public datasets.