A large-diameter peg-in-hole assembly strategy based on deep reinforcement learning and fuzzy strategy was proposed, in order to address the problems of large inertial impact, unstable force control, and poor assembly accuracy in the large-diameter peg-in-hole assembly task. In this strategy, the assembly actions output from the reinforcement learning algorithm were compensated by a fuzzy action generator to achieve accurate state tracking. The deep deterministic policy gradient (DDPG) algorithm was used to acquire the environmental state data and calculate the output actions, by which the robot’s assembly state was changed. A fuzzy action generator was introduced to combine with DDPG algorithm to generate DDPGFA assembly strategy, and the fuzzy strategy was utilized to add action coefficients to improve the accuracy of assembly actions. Based on the rational formulation of reward function and fuzzy rules, the rapid convergence of the training process was realized. System stresses were secured during e-learning by setting safety thresholds. Simulation and experimental results of large-diameter peg-in-hole assembly showed that, the DDPGFA strategy can complete the assembly in a more stable number of steps, and the offline training speed was increased by about 15% and the assembly contact force was reduced by about 30% compared with the reinforcement learning assembly strategy without fuzzy actions.