Quantitative phase microscopy is capable of achieving nondestructive and label-free imaging of transparent samples, rendering it suitable for biological cell research. However, the coupling of sample refractive index and physical thickness cannot be presented separately in phase data. Decoupling methods require tedious experimental and computational processes and thus cannot meet the needs of automated real-time detection in biomedical research and applications. To address this issue, this study constructs a new semantic segmentation network based on U-Net by adding the attention mechanism under the idea of a residual structure and dense connection module. This enables exploration of the method of decoupling the physical thickness and refractive index of uniform medium samples based on a single-phase map. The model was trained on a dataset comprising polystyrene microsphere phase maps, and phase data decoupling was achieved for samples of mature red blood cells with different geometric features. The relative error of the average refractive index obtained via single-frame phase separation was 0.9%. This method requires only a single-phase map of the sample in any direction and trains a neural network model using standard samples for highly specific quantitative extraction of chemical and physical information from biological cell samples. Moreover, it has the characteristics of convenient data collection and low computational complexity and can serve as a reference for automated quantitative analysis of phase information.