Abstract: Provided are a hologram model training method and apparatus based on an unlabeled sample training set, a device, and a medium. The hologram model training method includes: obtaining an unlabeled sample training set containing a plurality of red/green/blue-depth (RGB-D) images; iteratively training a hologram model based on the unlabeled sample training set until a preset iteration quantity is reached: in a training process, calculating a multi-depth diffraction field matrix of each RGB-D image in the training set at a preset diffraction distance, and inputting the multi-depth diffraction field matrix into the hologram model to generate a multi-depth hologram; reconstructing the multi-depth diffraction field through layer-by-layer diffraction by using an angular spectrum method (ASM), and obtaining a focus-stacked image; comparing a difference between a target RGB-D image and the focus-stacked image, calculating a model loss, and optimizing a parameter of the hologram model based on the model loss.