烧蚀热防护是高超声速飞行器重要的热防护手段，多孔热解炭化材料是一种烧蚀性热防护材料，其渗透率对输运特性有着显著影响。针对防热多孔材料渗透率公式中经验系数难以获取的问题，构建了包含材料微观结构图像、宏观结构特征参数（最大流与分形维数）的多源异构数据集，采用直接蒙特卡洛模拟粒子类方法计算微结构渗透率，并在此基础上提出了三种基于多源异构数据融合策略的渗透率预测方法：基于决策层的融合策略、基于多源数据特征直接拼接的融合策略、基于多源数据跨模态注意力机制的融合策略。通过比较三种不同融合策略的模型预测性能，由于基于跨模态注意力机制的特征层融合策略能够捕捉图像卷积特征与结构特征参数特征之间的关系，动态调整结构图像与结构特征参数之间的权值，其模型预测效果最优，在预测集上决定系数（R2）为0.95，平均绝对百分比误差（MAPE）为5.29%，且与单源数据驱动的渗透率预测模型相比，R2提高了6%，MAPE下降了41%，能够高效准确地对渗透率进行预测，为实际高超声速飞行器热防护结构精细化设计提供技术支持。

Ablative thermal protection is an important thermal protection method for hypersonic vehicles. Porous pyrolytic car-bon materials are a type of ablative thermal protection material, whose permeability significantly influences transport characteristics. To address the issue of difficulty in obtaining empirical coefficients in formulas of permea-bility for thermal protection porous materials, a multi-source heterogeneous dataset was constructed, incorporating material microstructure images and macroscopic structural characteristic parameters (maximum flow and fractal dimension). A particle-based method named direct simulation of Monte Carlo was employed to calculate the per-meability of the microstructure. Based on this, three permeability prediction methods utilizing multi-source hetero-geneous data fusion strategies were proposed: a decision-level fusion strategy, a fusion strategy based on direct concatenation of multi-source data features, and a fusion strategy based on a cross-modal attention mechanism for multi-source data. By comparing the predictive performance of the three different fusion strategies, it was found that the feature-level fusion strategy based on the cross-modal attention mechanism, which captures the relation-ship between image convolutional features and structural characteristic parameters and dynamically adjusts the weights between structural images and structural characteristic parameters, achieved the best predictive perfor-mance. On the test set, the coefficient of determination (R2) was 0.95, and the mean absolute percentage error (MAPE) was 5.29%. Compared with single-source data-driven permeability prediction models, R2 improved by 6%, and MAPE decreased by 41%. This method enables efficient and accurate permeability prediction, providing tech-nical support for the refined design of thermal protection structures in practical hypersonic vehicles.