Understanding 4D point cloud videos is crucial for intelligent agents to perceive the dynamic changes in their external environment. However, due to the inter-frame time inconsistency and spatial disorder inherent in long-sequence point clouds, designing a unified 4D global model faces significant challenges. Existing methods primarily rely on static, monolithic network architectures that apply a uniform computational pipeline to all input data. This approach neglects the differences in spatio-temporal complexity across videos, resulting in inefficient resource allocation and limiting model’s performance. To address these issues, we present a novel content-aware 4D point cloud processing approach, termed DSTA4D, which leverages dynamic spatio-temporal decoupling via adaptive modules. We first propose decoupling temporal and spatial features within the embedding layer, which avoids the complexity of full-process long-term modeling. Second, we introduce a innovative lightweight module: Dynamic Spatio-Temporal Adapter (DST-Adapter). This module dynamically generates gating weights based on the global spatio-temporal features of the input sequence and adaptively fuses features from three parallel streams: identity path, spatial enhancement path, and temporal enhancement path. This content-aware mechanism allows the model to intelligently allocate its computational focus to the most critical feature dimensions. Our experiments on mainstream benchmarks show significant performance gains, offering a more efficient and intelligent adaptive modeling paradigm for point cloud video understanding.