Causal inference has emerged as a promising approach for identifying decisive semantic factors and eliminating spurious correlations in visual representation learning. However, most existing methods rely on latent, data-driven confounder modeling, normally attributing the source of bias to background information while neglecting object-level semantic confusions that commonly occur in complex scenes. This limits their effectiveness in disentangling causal factors from confounding semantics. To address this challenge, we propose an explicit modeling approach for both causal factors and confounders, termed Explicit Modeling Causal Model (EMCM). The proposed framework consists of three key components. The Features Stability Estimation module explicitly models the relationship between visual semantics and class labels by leveraging clustering patterns to perform class-aware separation of causal and confounding factors. It produces class-specific causal factors and confounding factors linked to ambiguous categories. Subsequently, the Discriminative Features Enhancing module integrates causal factors into fused patch features via front-door intervention for stable semantics. In parallel, the Explicit Confounder Modeling and Debiasing Module learns confounders under clear label guidance and derives debiased context features by TDE modeling. This framework leverages two complementary causal perspectives to construct a unified semantic representation that facilitates improved generalization. Extensive experiments on two datasets demonstrate that EMCM effectively disentangles causal and confounding factors in complex scenarios, consistently outperforming state-of-the-art causal debiasing methods and text-guided methods in all metrics.