<p>Polymers often exhibit multi-state conformational transitions with multiple pathways as temperature varies. However, characterizing the inherent features of these pathways is hindered by the lack of physical characterizations that can distinguish various transition pathways between complex and disordered states. In this work, we introduced a machine-learning framework based on spatiotemporal point-cloud neural networks to identify and analyze conformational transition pathways in polymer chains. As a case study, we applied this framework to the temperature-induced unfolding of a single semi-flexible polymer chain, simulated <i>via</i> coarse-grained molecular dynamics. We first combined spatiotemporal point cloud neural networks and contrastive learning to extract features of conformational evolution, and then we employed unsupervised learning methods to cluster distinct transition pathways and unfolding trajectories. Our results reveal that, with increasing temperature, semi-flexible polymer chains exhibit five distinct unfolding pathways: rigid rod → random coil; small toroid → large toroid → hairpin → random coil; rod bundle → hairpin → random coil; hairpin → random coil; and tailed structure → random coil. We further calculated the structural order parameters of those typical conformations with distinct transition pathways, we distincted five transition mechanisms, including the straightening of rigid rods, tightening of small rings, expansion of hairpin ends, symmetrization of rod bundles, and retraction of tailed structures. These findings demonstrate that our framework presents a promising data-driven approach for analyzing complex conformational transitions in disordered polymers, which might be potentially extendable to other heterogeneous systems like intrinsically disordered proteins.</p>