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• A CAE-based unsupervised deep learning network is deployed for damage identification of a seismic-isolation structure. • Feature extraction performance of the network is investigated through real-time data training and earthquake verification. • A FE model is established to update the network and generate vibration datasets for test on structural diagnosis. • The test results reveal the relationship between the damage condition and the scoring indicators. Seismic-isolated systems have been investigated and practiced globally to prevent the destruction of existing structures, and vibration-based structural health monitoring is important for maintaining the functionality of the isolation systems. Given the recent boom in computational science and machine learning algorithms, this paper deployed a structural health monitoring system for a rubber bearing-isolated gymnasium in an area with high seismic fortification intensity and introduced an unsupervised deep learning network named convolutional autoencoder (CAE) to identify damage from vibrations of the isolation layer. The CAE network is first trained by operational monitoring vibrations and tested by new data. The vibration assessment results indicate that the CAE network can accurately reconstruct daily data and effectively detect unexpected ground motion. Thereafter, to further test the network's damage identification and localization performance, an analytical finite element model of the gymnasium structure is established, and the undamaged and damaged datasets are generated for network verification and updating. The verification results demonstrate that the CAE network leads to reliable feature extraction power in global and local damage detection of the isolation layer. The proposed vibration assessment method is beneficial to building administrators in making accurate and timely decisions with the assistance of the score result provided by the CAE network. [ABSTRACT FROM AUTHOR]