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Most data collected during routine monitoring of railway station buildings are categorized as in good health, with only a small percentage of samples corresponding to a damaged state. Standard deep neural networks (DNNs) process target samples regarded as normal samples. When typical DNNs are confronted with imbalanced samples, namely, samples with a low proportion of abnormal samples (e.g., damaged stage), this can lead to a high rate of misdiagnosis. To solve this problem, this paper suggests a novel damage monitoring model for railway station buildings, called cluster-based undersampling and Siamese artificial neural network. In this model, normal samples are undersampled based on clustering, and the clustering center and damage samples are combined in a training dataset to reduce the imbalanced rate of damage samples. SANN maps the original space to a feature space where imbalanced damage states are easier to distinguish. Based on SANN, a data-pair selection strategy based on a feedback mechanism is designed to sufficiently mine sample characteristics. A loss function that combines similarity calculation and multi-classification cross-entropy is proposed to improve the representation and classification performance of the model. Six popular algorithms are compared through experiments based on the actual monitoring datasets obtained at the Shijiazhuang Railway Station in China. For different imbalance ratios, the proposed model displayed the highest performance in all indicators. The recall rate for certain imbalanced classifications increased by over 30% compared to the baseline method, verifying the effectiveness and superiority of the model in identifying imbalanced damage states. [ABSTRACT FROM AUTHOR]