本文提出了一种基于深度学习的复合材料结构性能参数反演方法。传统的复合材料性能测试方法通常需要大量的实验资源，而本文将有限元仿真与神经网络模型相结合，实现了复合材料层合板参数的高效准确识别。本研究探索了两种深度学习模型在参数反演中的性能，并分析了模型超参数、特征类型和数量对反演精度的影响。结果表明，所提方法能够准确识别复合材料的弹性模量、泊松比、强度等参数，除泊松比外，误差均小于10%，验证了基于深度学习的复合材料参数反演方法的有效性。此外，本文还分析了数据集构成和铺层信息对参数反演精度的影响，为进一步优化复合材料结构设计提供了理论依据。

This paper proposes a deep learning-based inversion method for determining the structural performance parameters of composite materials. Traditional testing methods for composite material performance typically require substantial experimental resources. In contrast, this article integrates finite element simulation with neural network models to achieve efficient and accurate identification of composite laminate parameters. This study evaluates the performance of deep learning models in parameter inversion and analyzes the effects of model hyperparameters, feature types, and quantities on inversion accuracy. The results demonstrate that the proposed method can accurately identify the elastic modulus, Poisson's ratio, strength, and other parameters of composite materials, with errors of less than 10%, except for Poisson's ratio, thus validating the effectiveness of the deep learning-based composite material parameter inversion method. Additionally, this article examines the impact of dataset composition and layer information on the accuracy of parameter inversion, providing a theoretical foundation for further optimization of composite material struc-ture design.