<p>Intensity diffraction tomography is a label-free and non-invasive three-dimensional microscopy imaging technique, widely used in the analysis of cell morphology and study of physicochemical properties. However, the existing intensity diffraction tomography techniques often require complex computations and converge slowly, particularly when applied to samples with multiple scattering. To address the above issues, this paper presents a multi-layer propagation neural network model that incorporates the obliquity factor into forward propagation. In this model, the refractive index distribution of biological samples is represented as learnable parameters. Using the propagation equations, the predicted intensity at the measured plane is obtained, and the reconstruction process is fully automated, simplifying gradient computation and enhancing reconstruction efficiency. The experimental results demonstrate that, compared to existing multi-layer propagation models, our approach increases the average reconstruction speed by 5% to 40%, significantly reducing reconstruction time and facilitating faster cell analysis.</p>