Porous sandwich structures, particularly aluminum foam sandwiches (AFS), are widely used in lightweight and impact-resistant applications, yet their mechanical performance remains difficult to predict due to irregular and multiscale pore morphologies. Traditional constitutive models and current deep learning methods fall short in capturing the complex structure–property relationships of those materials. Accordingly, this work proposes a three-dimensional (3D) pore cloud representation learning method tailored for energy absorption prediction. A novel digital descriptor, termed the pore cloud, is constructed from 3D scans of real AFS cores to preserve detailed pore-level geometric and topological information. A comprehensive structure–property dataset is subsequently generated by integrating these pore cloud features with energy absorption data obtained through finite element analysis (FEA). Furthermore, this work develops PoreNet, a point cloud-based deep learning architecture that learns the direct mapping from mesoscale pore morphology to macroscopic mechanical response. The experimental results demonstrate that PoreNet achieves a high prediction accuracy of 95.12%, robust generalization across variable porosities, and fast convergence within 30 min on a consumer-grade Graphics Processing Unit (GPU). It outperforms both traditional analytical models and baseline neural networks. In addition, this study demonstrates the effectiveness of pore-level geometric learning in structure–property modeling and offers a scalable, data-driven framework for the design and optimization of advanced porous sandwich composites. The dataset and the proposed algorithm are publicly available at https://crescentrosexx.github.io/pore-net/.