Abstract: Probabilistic prediction of offshore wind power is not high in accuracy due to the predetermined threshold limitation of the traditional feature correlation method and the magnitude difference of the quantile loss in each quantile loss. To improve the probabilistic prediction accuracy, a multi-task joint quantile loss-based dual-attention probabilistic prediction model (MT-DALSTM) is proposed. Firstly, a feature and temporal dual attention mechanism is introduced to mine the correlation and temporal dependence among features, and attention weights are given to key features and time point information to improve the accuracy of power prediction. Secondly, during model training, the multi-task joint quantile loss based on task uncertainty is used to improve the final prediction results by dynamically adjusting the proportion of each loss weight. Finally, the simulation validation results based on the real data from the Donghai Bridge offshore wind farm show that the proposed method has significant improvement in sharpness, reliability and comprehensive performance indexes compared to the existing wind power probabilistic prediction studies, which verifies the effectiveness of the model in improving the prediction accuracy.