To improve the accuracy of short-term wind power prediction, a BiLSTM network integrating a single-head attention mechanism(SA) and an improved northern goshawk algorithm for parameter are proposed. Firstly, wind power data is preprocessed, and the correlation degree between each factor and wind power is calculated by using the Pearson correlation coefficient method. The factors high correlation degree are retained to improve the prediction accuracy of the model. Secondly, a single-headed attention mechanism is introduced to capture long-range dependencies in the sequence, which increases the generalization ability of the model. Finally, in view of the problem of difficult hyperparameter selection of BiLSTM, the improved northern goshawk algorithm which integrated refraction reverse learning initialization and the of positive cosine is used to optimize the three super parameters of the number of hidden units, the maximum training cycle and the initial learning rate in the model, and the INGO_LSTM_SA model is used to predict after obtaining the optimal parameters. Experimental verification is carried out through the data of a wind power station in Xinjiang. The coefficient of determination of the proposed model is 2.08% than that of the original BiLSTM network, and the root mean square error and the mean absolute error are reduced by 23.0% and 24.8% respectively.