Intelligent fault diagnosis technology can effectively guarantee the safe operation of mechanical equipment. Traditional bearing fault diagnosis generally assumes that the labeled source and unlabeled target domain data follow the same distribution. However, the conditional and marginal distributions of bearing data usually do not satisfy the same distribution assumption in actual diagnosis scenarios. Moreover, feature distortions are difficult to eliminate when performing adaptive distribution alignment in the original Euclidean space, which affects the fault diagnosis performance. In this paper, an adaptive bearing fault diagnosis model based on manifold feature learning and dynamic distribution alignment is proposed to address these challenges. First, we construct a geodesic flow kernel in the Grassmann manifold and extract the inherent manifold feature representation associated with the bearing fault information, avoiding data feature distortions. Second, a crossdomain adaptive factor is defined by distance to dynamically evaluate the conditional and marginal distributions of manifold features. Finally, a crossdomain classifier is solved iteratively to predict the target domain samples under the principle of structural risk minimization. The experimental analysis of multiple indicators shows that the model can effectively avoid feature distortions and use dynamic weights to adjust the relative importance of conditional and marginal distributions between crossdomain data, which verifies the effectiveness of the proposed method.