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The present application relates to the field of display technology and human-computer interaction, and provides a flat panel display visual comfort prediction method and system based on a multi-modal fusion model, wherein the method comprises the following steps: step one, collecting an original image set, for each original image, using a generative adversarial network model to convert an input random sequence into enhanced materials, and then fusing the enhanced materials and the original image at a preset ratio to obtain a plurality of new images; step two, collecting physiological features and physical features to obtain training samples; step three, training a multi-modal fusion model based on a stacked ensemble framework; and step four, inputting test data to obtain a visual comfort prediction result. The present application aims to solve the problems of a lack of high-quality labeled samples, a lack of coupling relationship in single-modal analysis, and poor scene adaptability in the field of flat panel display visual comfort prediction.