<p>Understanding the conformational characteristics of polymers is key to elucidating their physical properties. Cyclic polymers, defined by their closed-loop structures, inherently differ from linear polymers possessing distinct chain ends. Despite these structural differences, both types of polymers exhibit locally random-walk-like conformations, making it challenging to detect subtle spatial variations using conventional methods. In this study, we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers. By utilizing the Dynamic Graph CNN (DGCNN) model, we classify polymer conformations based on the 3D coordinates of monomers, capturing local and global topological differences without considering chain connectivity sequentiality. Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight, aligning with theoretical predictions. Additionally, interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences: cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis, while linear polymers show elongated ends with more spherical centers. These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern, providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.</p>