Analysis of the Geographical Environment for the Formation of Ethnic Traditional Sports Culture in South China and Its Integration with Ecotourism

Modeling geographically distributed systems requires effective characterization of multi-scale environmental features and complex interaction propagation across heterogeneous entities. This study proposes a Geospatial Cultural Adaptive Intelligent Framework (GCAS-EIF) integrating hierarchical spatial encoding, heterogeneous graph reasoning, and season-aware spatio-temporal attention mechanisms. A Hierarchical Spatial Grid Encoding (HSGE) module is developed to construct multi-resolution representations of terrain, climate, and ecological conditions through scale-aware attention fusion. An Ecocultural Heterogeneous Interaction Graph (EHIG) is then established to model relational information propagation among geographical regions, social groups, activity patterns, and environmental resources using relation-aware message transmission. To capture dynamic environmental variations, a Seasonally Aware Spatio-Temporal Attention Modeling (STACAM) module is introduced to characterize temporal dependencies associated with climatic fluctuations and periodic events. Experimental results demonstrate that the proposed framework improves interaction-matching accuracy by 14.2% while reducing computational complexity by 28.4% compared with conventional single-scale approaches. The proposed architecture provides an effective methodology for multi-scale spatial information representation, heterogeneous network propagation analysis, and adaptive spatio-temporal modeling in geographically distributed environments.