Accurate and reliable wind speed prediction is the key to efficient utilization of wind energy. In consideration of the inherent instability of wind speed, a SWD-Transformer-KAN prediction model is proposed by fusing Swarm Decomposition (SWD), Transformer, and Kolmogorov-Arnold Network (KAN). Firstly, the original wind speed data is decomposed using the SWD method to extract key features. Secondly, a Transformer-KAN prediction model is established for each decomposed subsequence, which fully utilizes the temporal processing capability of Transformer and the nonlinear approximation capability of KAN. Finally, the resulting wind speed prediction values are obtained by superimposing the predicted results of all subsequences. To verify the effectiveness of the proposed model, it is experimentally compared with other models. The experimental results show that the SWD-Transformer-KAN model has the best predictive performance, with a determination coefficient as high as 99.91%.