Abstract: The utilization of neural networks for the prediction of alloy properties and the inverse design of alloy microstructures has emerged as a novel approach in the industry for understanding material performance and developing new alloys. Texture acts as a critical factor influencing microstructural evolution during alloy deformation. It is typically characterized by spatially uncorrelated discrete grain orientation Euler angles, spatial-orientation coupled Euler angles within a Representative Volume Element (RVE), or pole figures/inverse pole figures. However, identifying which texture representation method serves as the optimal input to maximize the performance of neural network models requires further investigation. Consequently, employing a modified U-Net model as the backbone architecture, this study evaluates and compares the impact of three texture representation methods - discrete Euler angles, spatial Euler angles, and pole figures-as model inputs on the overall performance of the neural network. The three trained neural network models were individually deployed to predict samples within the test set. The results demonstrate that employing pole figures as the texture representation method yields the optimal performance. Furthermore, the trained neural network models were utilized to predict the macroscopic stress-strain curves of the alloys by incorporating a one-dimensional (1D) convolutional layer at the output stage. Compared to traditional methods relying solely on fully connected layers, this modification significantly enhances the prediction accuracy of the stress-strain curves.