Polyurethane (PU) is an excellent thermal insulator, and incorporating Phase Change Material (PCM) capsules into PU significantly enhances building envelope performance by improving indoor thermal stability and reducing temperature fluctuations. We propose a hierarchical multi-scale model using Physics-Informed Neural Networks (PINNs) to accurately predict and analyze the thermal conductivity of PU-PCM composites at both micro and macro scales. This approach effectively addresses complex inverse problems and multi-scale phenomena, offering insights that optimize material design. A case study further demonstrates the model's potential in improving thermal comfort and reducing energy consumption in buildings. The successful development of this PINNs-based model holds great promise for advancing PU-PCM applications in thermal energy storage and innovative building insulation design.