Abstract: A Wasserstein distance and difference metric-combined chest radiograph anomaly identification domain adaptation method and a corresponding system are provided. The method includes the following steps: step 1, data preparation and data pre-processing for chest radiographs; step 2, multi-scale feature extraction based on a swin transformer network; step 3, loss minimization based on a Wasserstein distance and a contrastive domain discrepancy; and step 4, using the model to perform chest radiograph prediction after verifying the model. The method selects source domain samples closest to target domain samples, narrows a distance of the same class between the target domain samples and the source domain samples in feature space, and expands a distance between different classes. Meanwhile, a classification prediction task for the chest radiographs is performed by using the multi-scale features, improving a receptive field and capturing more information conducive to the classification prediction task.