Objective Forest resources constitute the material foundation for social development and a crucial guarantee for sustainable development. Analyzing the spatial distribution characteristics of forest cover and its influencing factors provides valuable insights for protecting and developing forest resources.

Method Using provincial-level administrative units as the basic research units and integrating regional fundamental data, this study investigated the spatial distribution characteristics of forest cover at the national scale based on ArcGIS Pro and GeoDa platforms. Spatial autocorrelation analysis was employed to reveal the heterogeneity in the spatial distribution of forest cover. Significant influencing variables were screened through multicollinearity tests. Multiple linear regression (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models were constructed and compared to evaluate their applicability in explaining the factors influencing forest cover.

Result (1) Forest cover exhibited significant positive spatial dependence, with a global Moran’s I index of 0.502, Z = 4.34 (p < 0.001); (2) Based on model evaluation indicators, compared with OLS and GWR models, the MGWR model performs best in analyzing the influencing factors, showing the highest R² and adjusted R² and the lowest AICc; (3) MGWR-based analysis indicates that precipitation is positively correlated with forest cover rate spatially, while sunshine duration and average soil pH are negatively correlated. Natural factors have greater influence on forest cover rate in western regions, whereas the eastern regions are more affected by socioeconomic activities.

Conclusion (1) Forest cover rate displays spatial imbalance with an overall “southeast–northwest” decreasing gradient and a clustered spatial pattern. Local spatial associations mainly consist of high-high and low-low clusters, suitable for multiscale geographically weighted regression analysis; (2) The MGWR model shows superior performance in analyzing forest cover rate influencing factors, explaining 81.2% of the spatial variation according to the adjusted R².