Abstract: Under the development of new power systems, it is of great significance to extract the key response features strongly related to the stability of transient voltage with an effective feature selection for the studies of the mechanism of transient voltage instability and the potential security risks of the system. Therefore, a new feature selection method is proposed based on the composite framework of the improved filtering method and the hybrid element heuristic packaging method. The improved Max-Relevance and Min-Redundancy criterion of symmetric uncertainty value is firstly used to have a coarse screen of the features. Then the Q-learning reinforcement learning is integrated into the meta-heuristic optimization algorithm, and the exploitation and exploration compromise strategy is used to enhance the feature fine selection ability to obtain the optimal critical response feature subset. On this basis, the Shapley additive explanation is applied to comprehensively analyze the influences of each of the screening features on the transient voltage stability and the weak links of the system. The effectiveness of the proposed method is verified by an example of a new power system.