IntroductionThe increasing interconnectedness of global societies highlights the need to understand the dynamic relationships among cultural exchange, environmental awareness, and geoscientific communication. Geoscience provides important insights into Earth systems and human environmental interactions, but its communication and interpretation are often shaped by cultural context, local knowledge, and social practices. Traditional modeling approaches often have limited capacity to capture the temporal, spatial, and interactional complexity of these relationships, which restricts their predictive accuracy and adaptability.MethodsTo address these limitations, this study proposes the Manifold Event Forecaster, a methodological framework for modeling and forecasting the interactions between cultural and environmental factors. The framework consists of three core modules: the Counterfactual Temporal Decomposer, which separates temporal patterns and underlying trends; the Agent driven Interaction Planner, which models interactions among heterogeneous agents; and the Uncertainty Propagation Filter, which estimates and propagates uncertainty to improve predictive reliability. Two additional strategies, Constrained Optimization Refinement and Episodic Retrieval Adjustment, are incorporated to enhance robustness under dynamic and heterogeneous conditions.Results and DiscussionExperimental results show that the proposed framework improves predictive performance by more than 15% compared with baseline models. Rather than treating cultural or environmental factors as deterministic explanations, the framework identifies temporal associations and forecasting patterns from structured and partially structured data. The findings provide a computational tool for analyzing cultural exchange and environmental awareness, with potential applications in geoscience communication, sustainability research, and policy oriented environmental analysis.