Focusing on the problem of UAV maneuvering target tracking in complex obstacle environment, this paper proposes a navigation and tracking strategy based on IMM-PPO. The state information of maneuvering targets with multiple models is estimated, a reward and punishment function based on target tracking performance, tracking approaching time and obstacle constraints is designed. The algorithm framework of near end strategy optimization is designed under the Actor-Critical network structure, and the network parameters under the maximum reward are trained through the interaction between agents and the environment. The trained tracking strategy network can complete obstacle avoidance navigation and achieve stable tracking of maneuvering targets according to environmental information. The simulation results show that, compared with the traditional obstacle avoidance and tracking algorithm, the navigation and tracking strategy based on IMM-PPO has better tracking performance, faster tracking speed, and shorter obstacle avoidance navigation path. The algorithm also has a certain degree of autonomous tracking ability when the initial conditions change, and has greater advantages when applied to the UAV maneuvering target tracking task.