In order to improve the operational efficiency and voltage quality of distribution networks, an optimization strategy is proposed based on a state-response framework, combining deep reinforcement learning with the distributed generalized fast dual ascent （SAC-GFD） algorithm. Firstly, the soft actor-critic （SAC） algorithm is employed to model the distribution network operation optimization problem as a Markov decision process （MDP）. The agent interacts and explores in an environment with renewable energy fluctuations and load uncertainties, obtaining a control strategy that is robust to uncertain environments. The distribution network operation optimization problem is thus transformed into a Markov decision process, enabling the training of an agent capable of quickly outputting the optimal active and reactive power for distribution network equipment. Secondly, the current distribution network's power flow distribution, node voltage states, and active and reactive voltage sensitivity matrices are calculated. Then, based on the current state of the distribution network, users apply a distributed method to calculate their optimal load adjustments to ensure the safe operation of the network. Finally, the simulation results on the IEEE 33⁃node system demonstrate that compared to traditional deep reinforcement learning algorithms, the proposed method more effectively reduces network losses and node voltage deviations while achieving faster training speeds and better optimization results.