Accurate wind speed forecasting can mitigate wind power fluctuations, enhance grid dispatch efficiency, reduce operational and maintenance costs, improve system accommodation capacity, and ensure secure and stable power system operation. This study proposes a hybrid framework integrating Bayesian Discrete Wavelet Packet Transform denoising with Kolmogorov-Arnold Network for short-term wind speed forecasting. The framework initially employs BDWPT’s multiscale decomposition capability and integrates with Bayesian unbiased hypothesis testing to perform adaptive denoising on raw wind speed data, thereby enhancing the data representation capability. Subsequently, a KAN-based neural architecture is constructed, leveraging learnable spline-based activation functions to achieve high-precision modeling of complex nonlinear relationships, effectively capturing both temporal dynamic characteristics and abrupt variation patterns in wind speed sequences. The efficacy of the proposed hybrid model is substantiated through rigorous comparative experiments. Results from two distinct datasets demonstrate that the hybrid model surpasses the original baseline models across three key evaluation metrics in the context of short-term wind speed forecasting. Additionally, comparative analyses with alternative denoising techniques paired with KAN reveal that the BDWPT-KAN configuration outperforms other combinations, thereby validating the effectiveness of integrating BDWPT with KAN.