Inefficient evaluation methods for germplasm traits severely hinder the development, utilization, and preservation of germplasm resources. Addressing the current lack of high-throughput measurement techniques for the traits of Caulerpa lentillifera, this study established a nondestructive quantification method for the spherical branchlets count of C. lentillifera based on CT imaging and the PointNet++ model. First, X-CT systems were employed to perform 3D imaging of 196 fronds, enabling rapid and nondestructive acquisition of their three-dimensional structural data. The obtained data were converted into 3D point cloud format, and a dataset was constructed by manually annotating the point cloud data of 196 fronds using the open-source software CloudCompare. The dataset was partitioned into training, validation, and test sets at a ratio of 7∶2∶1. Through model training based on the point cloud segmentation network PointNet++, along with multiple rounds of refined data processing and hyperparameter tuning, an optimal model was successfully developed. This model achieved high-throughput and accurate quantification of spherical branchlets on fronds. Finally, the model was tested on C. lentillifera accessions with densely clustered spherical branchlets, demonstrating high goodness-of-fit, with R², RMSE, and MAPE values of 0.927 6, 4.23, and 6.37%, respectively, confirming the feasibility of this technique. This study provides an effective tool for morphological trait measurement in C. lentillifera.