To solve the problem of low randomness of action selection in the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm,the normal distribution is used for action selection in the TD3 algorithm.Based on the interfered fluid path planning method‘s advantage of high path smoothness,a UAV path planning framework based on the interfered fluid and the TD3 algorithm is proposed.It is used for UAV path planning in unknown dynamic environment,which can realize fast convergence of the UAV path planning scheme.The simulation results show that the improvements to the algorithm dramatically enhance network training efficiency and meet the trajectory quality requirements while ensuring real-time obstacle avoidance,which provide new ideas for the application of Deep Reinforcement Learning (DRL) in path planning tasks.