A reasonable canopy structure and cultivation density of fruit trees can increase the amount of light interception within their canopies, which has an important impact on improving the yield and quality of fruit. A 3D point cloudbased model for predicting the canopy light distribution of group cherry trees was proposed by using a thin spindleshaped cherry tree as the research object. Firstly, the Azure Kinect DK camera was used to obtain the 3D point cloud data of the group cherry trees, and the complete 3D point cloud data of the group cherry trees were obtained through point cloud data preprocessing. Secondly, according to the actual cherry tree canopy segmentation method, the point cloud data of cherry tree canopies were point cloud stratified and the point cloud colour features of different regions were extracted. Again, a point cloud projection area calculation method based on the Delaunay triangulated concave packet algorithm was proposed to calculate the point cloud projection area of different regions through concave packet boundary point extraction and vector product fork multiplication. Finally, a model for predicting the light distribution in the canopy of group cherry trees was developed, which was a random forest model with point cloud colour characteristics and relative projected area characteristics as input and measured relative light intensity as output. The experimental results showed that the model was able to predict the light distribution in the canopy of cherry trees with a mean coefficient of determination of 0.885 and root mean square error of 0.0716. The research results can provide technical support for reasonable planting density management and automated pruning of cherry trees during dormancy.