Point clouds, unlike images represented by dense grids, are characterized by irregularity and disorder, making it difficult to precisely reason out the shape features in point cloud data. The internal-external shape son volution for point sets (IE-Conv) is proposed to address the limitations of current research. The local shape inside the point set is treated separately from the global shape outside the point set using an efficient bilateral design. Rich inter-point relationships are selectively studied in a gate-based manner within the point set, while point-by-point and local features are optimized by self-calibration functions; outside the point set, global shapes are constructed using graph convolution and focus on long-range dependencies between point sets. Finally, the organic fusion of the bilateral outputs is performed. This paper performs classification and segmentation experiments on the standard ModelNet40 and ShapeNet datasets by hierarchically embedding IE-Conv into the shape-reasoning convolutional network (SR-Net). The experimental results show that the classification task achieves an accuracy of 93.9% and the segmentation task achieves the mean intersection over the union of 86.4%, which verifies the good performance of SR-Net in point cloud analysis.