To solve the adaptability problem of transient stability assessment models after the changes in the operation or topology of power systems, the conventional feature transfer learning methods mainly focus on bringing the conditional or marginal distributions between the source and target domain datasets closer together, but fail to quantitatively evaluate the contribution of the two distributions to different domains, resulting in unsatisfactory model transfer performance. To address this problem, SENet attention mechanism and dynamic distribution adaptive algorithm are introduced, and a deep adaptive network transient stability assessment model update framework based on SE-DDAN transfer is constructed, which is improved from two aspects, namely, feature extraction and dynamic adjustment of distribution weights between different domains, to further enhance the transfer performance and adaptability of the assessment model. The model is tested on IEEE39-bus and IEEE140-bus systems and the simulation results that the proposed model has advantages in assessment accuracy, adaptability and transfer performance after updating.