Existing deformation prediction models for concrete dams, which rely on classical linear regression methods or shallow machine learning techniques, have significant shortcomings in extracting complex features from environmental factors and in learning the long-term dependencies of deformation-environmental factor relationships. To address this issue, this paper proposes a deformation prediction model based on the Inception module and attention mechanism-enhanced gated recurrent unit (GRU). The proposed model effectively combines the feature extraction capabilities of the Inception module with the long-term dependency learning capabilities of GRU, enabling it to extract features from monitoring sequences of dam environmental factors across different scales and to predict the long-term deformation of the dam. Additionally, by incorporating the attention mechanism, the model reduces the risk of overfitting when learning features from multiple environmental factors. Validation results from an extra-high concrete double-curved arch dam project demonstrate that the proposed model outperforms other common shallow and deep learning models at typical monitoring points, making it suitable for concrete dam deformation prediction.