Skeleton-based action recognition has wide applications, but its performance degrades significantly with subtle action variations. This deficiency primarily arises because existing methods overemphasize adjacent joint relationships while neglecting non-adjacent correlations and dynamic contact states. To overcome these limitations, we propose topological feature filtering (TFF), which explicitly models both adjacent and non-adjacent correlations through learnable topological filters. The framework dynamically processes skeletal data via: (1) a topology-aware feature filtering with adaptive kernel density estimation for probabilistic selection, and (2) Comparative verification of contact-aware topological consistency models for fuzzy samples. On two benchmark datasets NTU 60 and NTU 120, the proposed topological filtering demonstrates superior accuracy-efficiency trade-offs, outperforming existing methods in both recognition performance and computational overhead.