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The Xception model extracts both fine-grained details and global semantic information, while PCA selects the most discriminative principal components to reduce redundant features, enhancing classification accuracy and efficiency. Experiments conducted on Eastern and Western painting datasets with 10-fold cross-validation demonstrate that the proposed model outperforms state-of-the-art methods, including EfficientNet, RegNet, and ConvNeXT. Notably, it achieves an average classification accuracy of 0.973 in Western painting classification. Moreover, the integration of PCA significantly reduces computation time, with classification speeds of 105\u00a0ms for Eastern paintings and 100\u00a0ms for Western paintings, surpassing benchmark models. This study presents an efficient and precise solution for automated art style classification, demonstrating the effectiveness of combining deep learning with dimensionality reduction. 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