With the rapid development of laser weapon technology, predicting the damage behavior and failure process of laser protective coatings plays a key role in understanding the reliability of coatings. However, current models have not fully studied the thermal response process of multi-layer composite coatings and heavily rely on data samples such that their stability and applicability still require further consideration. To tackle these problems, in this work, a multidimensional prediction framework combining finite element simulation, SHAP quantitative analysis and deep learning was proposed. Firstly, through thermal-structural coupled finite element simulation and the SHAP quantitative method, the surface reflectivity was identified as the dominant factor in suppressing temperature rise, revealing its significant negative correlation with the peak temperature; when the reflectivity was increased from 87% to 97%, the peak temperature under 3000 W laser irradiation was decreased by approximately 73.1%. Subsequently, experiments on the Y₂O₃/8YSZ/NiCrAlY multilayer coating system further validated the finite element model. The results indicated that no obvious damage occurred on the coating surface under low-power laser irradiation. As the laser power increased, significant microstructural evolution, such as ablation rings and elemental enrichment from the bond coat, appeared on the coating surface, showing good agreement between the experimental phenomena and simulation results. Finally, the constructed deep learning framework achieved high-precision prediction of thermal damage, where the deep neural network demonstrated high prediction accuracy for transient temperature curves (R²＞0.95) with a failure time prediction error of less than 10%. Furthermore, the Pix2PixHD network successfully realized high-fidelity inversion of the temperature field contour maps. This study provides a systematic reference for the structural design, performance evaluation and lifetime prediction of anti-laser coatings.