Many unfavorable factors such as system failure and sensor aging can often lead to deviation or even lack of monitoring data. In the present study, a bi-directional CNN-BiLSTM-Attention missing data interpolation model for intermediate missing data during dam monitoring based on the deep learning framework was constructed from the perspective of time series. The characteristics of the convolution neural network and long-term and short-term memory neural network were combined to extract time features in the model. The attention mechanism was introduced to optimize the interpolation process. The forward and backward interpolation results were fused by the weights decreasing according to time steps. A concrete gravity dam was selected to verify the effectiveness of the proposed model by interpolating the long-series missing data of dam monitoring. The results indicate that bidirectional fusion interpolation can effectively overcome the cumulative error of interpolation time steps for long-series missing data, and this deep learning framework has higher interpolation accuracy compared to other interpolation models.