Ferroelectric materials have emerged as significant research hotspots within the field of materials science and engineering, primarily due to their unique electrical properties. However, the electrical characteristics of these materials are influenced by various factors, including material composition, microstructure, and preparation processes, which introduce considerable randomness and uncertainty. Traditional experimental and simulation methods are often insufficient for capturing these complex interactions, thereby hindering the prediction and optimization of material performance. This paper presents a novel approach for predicting the electrical properties of ferroelectric materials by utilizing deep neural networks (DNNs). The DNNs are trained using experimental data and serve as a proxy model to predict critical electrical properties, such as the dielectric constant and dielectric peak. The (1-