For the poor maneuverability of flapping wing micro aerial vehicles(FWMAVs), a deep reinforcement learning(DRL) based local path planning method(IL-PPO2) is proposed with the assistant of demonstration learning in an unknown environment. Firstly, due to the limited visual angle of a stereo camera on a FWMAV, a“Heart” algorithm is proposed to reduce the requirement for control accuracy and meanwhile improve robustness. Then, according to the characteristics of the Heart algorithm, a U trap avoidance framework is developed. Finally, with the help of demonstration learning, a DRL based local path planning method is put forward, which is realized with the combination of the Heart algorithm and local planner. According to the simulation results, compared to the TD3fD DRL method, the path planning efficiency and success rate of the IL-PPO2 is higher than the TD3fD with shorter training time. Besides, compared to the dynamic window approach(DWA), the success rate of the IL-PPO2 is improved, and the path smoothness is promoted considering the integration of the Heart algorithm.