Abstract: Neutron diffraction has been recognized as an ideal analytical technique for investigating material properties and functions through the characterization of phase composition, structural features, and microstructural characteristics. Due to their electrical neutrality and magnetic moment, neutrons are endowed with significant advantages, including greater penetration depth, the identification of light elements, the distinction between isotopes, and the measurement of the magnetic structure of materials. Despite these merits, the practical application of neutron diffraction technology was confronted with several challenges. Fundamental limitations in neutron flux and detection efficiency were identified, making it a formidable task to enhance experimental efficiency while maintaining the accuracy of the results. With the rapid advancement of neutron diffraction techniques and the continuous expansion of materials science research, a substantial amount of experimental data generated by neutron diffraction experiments was accumulated rapidly. This growth posed substantial challenges to conventional data processing and analysis methodologies, necessitating the urgent exploration of new solutions using state-of-the-art technology. This study introduced the application of various machine learning (ML) algorithms in the domain of neutron diffraction techniques, covering single-crystal diffraction spectrometers, powder diffractometers, and residual stress diffractometers. Furthermore, the results of ML implementation in a neutron texture diffractometer developed in China were analyzed. In this research, a convolutional neural network (CNN) model was developed to learn from texture diffraction datasets, establishing a mapping relationship between diffraction data and crystallographic texture information. The methodology comprised three key phases: First, a deep-learning-based framework was designed for texture feature resolution. Subsequently, the critical regions in the pole figures that significantly influence the material’s texture were identified. Finally, the noise robustness of the model was evaluated systematically through the introduction of white-noise perturbations. Experimental results confirm that the proposed deep learning framework exhibits high accuracy in texture feature recognition from neutron texture datasets and maintains stable prediction performance under noise interference. This observation indicates that the approach provides a reliable technical path for the intelligent analysis of neutron diffraction texture data. The CNN-based texture volume fraction prediction method can generate accurate results rapidly. This breakthrough enables efficient texture characterization of face-centered cubic materials under low-flux conditions or through shortened measurement durations, thereby significantly enhancing measurement efficiency. The model’s superior anti-interference capability and rapid prediction speed are particularly suitable for advanced applications, notably in neutron in-situ tensile testing measurements, where real-time, high-precision texture analysis is crucial.