Abstract: The design of transparent thermal emitters requires a balance between high visible light transmittance and excellent radiative cooling performance. However, optimizing performance across multiple objectives typically involves introducing various functional materials. This significantly increases the complexity of material preparation and performance regulation, making inefficient trial-and-error experimental methods inadequate for meeting current demands. Furthermore, for multi-objective material design, the introduction of artificial intelligence (AI) technology requires primarily clarifying whether the design logic of the algorithm aligns with the physicochemical mechanisms of materials and improves the overall efficiency of material design. In this study, a transparent thermal emitter incorporating graphene quantum dots (TTEG) is developed using post-hoc explainable machine learning (PXML) and an active learning framework with prior knowledge constraints (ALL-PV). Six predictive models were established to estimate the fluorescence intensity of graphene quantum dots (GQDs). PXML was applied to identify the key factors governing the model decisions and to clarify the associated physicochemical mechanisms. The results showed that the reaction temperature and catalyst molecular polarity play decisive roles in determining the fluorescence intensity of GQDs in solvothermal growth systems. Based on this understanding, the reaction conditions and catalyst systems were optimized. The photoluminescence (PL) intensity of the resulting optimized sample, E-81, exceeded the highest value in the training dataset, and its photoluminescence quantum yield (PLQY) reached 38.9%. Subsequently, TTEG was fabricated from E-81 and tricyclodecanedimethanol diacrylate (DCPDA), with ALL-PV guiding the optimization of the preparation process. In ALL-PVs, prior knowledge constraints, surrogate models, and acquisition functions work together to direct each round of experimental selection. Within a design space of more than 390000 possible process combinations, this strategy determined the optimal preparation conditions after only five iterations and 60 experiments. Based on irradiance meter measurements, the fluorescence-induced transmittance reached 95.6%, demonstrating excellent thermal management performance. Furthermore, we employed the finite-difference time-domain (FDTD) method to simulate the TTEG, revealing the physical mechanism by which GQDs enhance the TTEG transmittance. Finally, the energy-saving performance and stability of the TTEG are evaluated. In Beijing, Kunming, and Haikou, the annual cooling energy consumption of the TTEG is 9.79, 8.75, and 11.46 MJ·m⁻² lower than that of ordinary glass, respectively. Simultaneously, the TTEG exhibits excellent chemical (Δτ_vis < 1%), thermal, and mechanical stability. Under indoor testing conditions, the power conversion efficiency of the solar cell (5 cm × 5 cm) increased by 17.6% (AM1.5, 100 mW·cm⁻²) when TTEG was used for solar cell encapsulation and its surface temperature was 9 ℃ lower than that of the glass-encapsulated solar cell. For a 41 cm × 30 cm commercial crystalline silicon solar cell under all-day illumination conditions (8:00 AM to 6:30 PM), its average operating temperature decreased by 3.4 ℃, with a maximum temperature drop of up to 7.0 ℃, while the maximum output power increased by approximately 10.8%. This study lays the foundation for the engineering development of transparent thermal emitters and highlights the potential of AI-guided design of multistep multiproperty materials.