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.