Abstract: To address the frequent issue of missing data in practical monitoring scenarios， high-accuracy imputation methods were required to ensure the reliability of monitoring and subsequent analysis. Therefore， a novel TPE Double Attention-Recurrent Neural Network （TDA-RNN） model was proposed， which integrated the Tree-structured Parzen Estimator （TPE） and attention mechanisms， aiming at imputing continuous missing wind speed data. By introducing the TPE optimization， the key hyperparameters of the model could be automatically optimized and selected. Meanwhile， the attention mechanisms could effectively capture feature information from the missing data. The model was validated using downburst wind speed data collected from field measurements at Texas Tech University. The results showed that， compared to traditional neural networks， the TDA-RNN model significantly improved imputation accuracy. When multiple segments of data were continuously missing， the root mean square error （RMSE）， mean absolute error （MAE）， and mean absolute percentage error （MAPE） of the data interpolation error index reached 0.72， 0.64 and 5.65%， respectively， demonstrating the high accuracy and robustness of the TDA-RNN model for data imputation.