To address the issue of performance degradation in rolling bearing fault diagnosis under varying operating conditions caused by distribution discrepancies between the source and target domains, this paper proposes a novel fault diagnosis method that integrates a multi-head self-attention mechanism with dynamic joint distribution adaptation. Firstly, a multi-head self-attention mechanism is incorporated into the feature extraction module to extract more discriminative and domain-invariant features from raw vibration signals. Secondly, maximum mean discrepancy and local maximum mean discrepancy are employed to align the marginal and conditional distributions, thereby reducing the distribution difference between the source and target domains. Finally, a dynamic weighting factor is designed to adaptively adjust the importance of marginal and conditional distribution alignment, enhancing the robustness and generalization ability of cross-domain fault diagnosis. The experimental results demonstrate that the proposed method achieved classification accuracies of 99.84% and 98.97% on two public datasets, significantly outperforming other approaches. Moreover, it maintained strong stability and robustness under severe noise interference, providing an effective solution for rolling bearing fault diagnosis.