We present a deep learning framework for modeling and analyzing the small-angle scattering data of polydisperse hard-rod systems, a widely used model for anisotropic colloidal particles. We use an autoencoder-based neural network to learn the mapping from the system parameters, such as the volume fraction, rod length, and polydispersity, to the scattering function. The dataset for training and testing such a neural network model is obtained from a Markov chain Monte Carlo simulation of 20 000 hard spherocylinders using the hard particle Monte Carlo package from HOOMD-blue. Four datasets were generated, each with 5500 pairs of system parameters and corresponding scattering functions. We use one of the datasets to investigate the feasibility of the learning and three additional datasets with different polydisperse distributions to demonstrate the generality of our approach. The neural network model transcends the fundamental limitations of the Percus–Yevick approximation by accurately capturing anisotropic interactions and high-concentration effects that analytical models often fail to resolve. This framework achieves significantly higher accuracy in reproducing scattering functions and enables a least-squares fitting routine for quantitative data analysis.