To enhance the accuracy of short-term wind power prediction， an xLSTM （extended Long Short-Term Memory）-Transformer model based on Bayesian optimization and feature fusion was proposed. In the proposed model， the temporal processing capability of Long Short-Term Memory （LSTM） network was integrated with the dynamic feature fusion ability of Transformer’s self-attention mechanism. By employing Bayesian optimization method， the hyperparameters of the model were optimized efficiently in a few iterations， thereby reducing the computational resources significantly. The experimental results demonstrate that， on a dataset from a wind farm in Inner Mongolia， when compared with the single LSTM model， Transformer model， Gated Recurrent Unit （GRU） model， and the xLSTM-Transformer model without Bayesian optimization and feature fusion， the proposed model achieves an average increase of 1.2% to 11.3% in the coefficient of determination （R2） compared to the benchmark models when the LookBack are 4 and 8； it also shows an average reduction of 12.8% to 38.4% in the Mean Absolute Error （MAE） and an average reduction of 8.6% to 35.8% in the Root Mean Square Error （RMSE）. The results indicate that the proposed model exhibits higher prediction accuracy and stability under conditions of short historical input.