Causal representation learning is a pivotal technology for achieving interpretability and intervenability in complex systems. However, current research faces several challenges: linear assumptions struggle to capture nonlinear causal dependencies in high-dimensional data, scarce labeled data limits model generalization. Notably, current frameworks exhibit deficiencies in both controllable intervention mechanisms and counterfactual inference capacities. Leveraging the crucial role of exogenous variables in explaining causal relationships and supporting counterfactual reasoning, this paper proposes a weakly supervised causal representation learning model driven by exogenous intervention and invertible flow. First, exogenous variables are introduced to simulate intervention scenarios, with causal graphs visually presenting causal paths and dependencies to achieve controllable intervention and counterfactual reasoning. Second, an invertible flow model is employed to capture nonlinear causal dependencies, overcoming the limitations of linear assumptions. Building on this, a dynamic weakly supervised alignment mechanism is incorporated, utilizing limited labeled data to constrain the semantic identifiability of causal factors, thereby mitigating the issue of scarce annotations. Experimental results on the Causal3DIdent dataset demonstrate significant performance improvements: the model achieves 94.5% accuracy in causal factor identification (8.8% increase over baseline models) and reduces intervention mean squared error to 0.015 (47.7% decrease) . Additionally, on datasets such as Pendulum-v1, the model exhibits strong performance, particularly in scenarios with limited labeled data, enabling effective causal inference and showcasing promising generalization capabilities and application potential.