Xia, YYongchareon, SLutui, RSheng, QZ2026-02-042026-02-042026-07-01Pattern Recognition, ISSN: 0031-3203 (Print), Elsevier BV, 175, 113146-113146. doi: 10.1016/j.patcog.2026.1131460031-3203http://hdl.handle.net/10292/20590Skeleton-based human activity recognition (HAR) has made significant progress through graph convolutional networks (GCNs) and Transformer architectures for spatiotemporal modeling. However, existing methods either employ predefined static graph topologies that cannot adapt to heterogeneous skeleton data or learn dynamic topologies based solely on local spatiotemporal features, thereby overlooking the global temporal frequency features of joint movements that are important for discovering semantically meaningful spatial relationships. We propose Frequency-Aware Topology Learning Graph Convolutional Network (FATL-GCN), a novel architecture that integrates frequency-aware temporal context to guide adaptive learning of spatial topology. Our approach leverages Time-to-Vector linear frequency encoding to capture both periodic and non-periodic motion patterns, employs frequency-guided topology learning to generate action-specific graphs through temporal-context-driven attention, and incorporates hierarchical multi-scale fusion for robust feature extraction across scales. Extensive experiments achieved top-1 accuracies of 93.8% (cross-subject) and 97.5% (cross-view) on NTU-60, 91.9% (cross-subject) and 93.1% (cross-setup) on NTU-120, and 51.7% on Kinetics-Skeleton. Ablation studies confirm the critical role of our components, with removing the dynamic graph topology causing a 3.5% accuracy drop and removing frequency-aware encoding causing a 2.1% drop.© 2026 The Author(s). Published by Elsevier Ltd. Creative Commons. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.46 Information and Computing Sciences4607 Graphics, Augmented Reality and GamesBioengineeringNetworking and Information Technology R&D (NITRD)0801 Artificial Intelligence and Image Processing0806 Information Systems0906 Electrical and Electronic EngineeringArtificial Intelligence & Image Processing4603 Computer vision and multimedia computation4605 Data management and data science4611 Machine learningJournal ArticleOpenAccess10.1016/j.patcog.2026.113146