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Traditional automatic flight path planning methods for UAVs cannot obtain all obstacle signals, making UAV flight unable to achieve obstacle avoidance effects, resulting in poor flight path planning effects. To this end, an automatic UAV flight path planning method based on Bayesian decision-making is proposed. The automatic planning module of the drone flight path includes an automatic planning module, an animation demonstration module, a map navigation module and a data export module. The automatic planning module is responsible for controlling the flight of the drone; the animation demonstration module uses the 240PRO model LEWITT sound card to show the flight path of the aircraft Provide sound; LS-TM8N map navigation module sends the radio frequency signal to the input end of the antenna through the serial port, and then the data export module exports and saves the relevant data. Based on the Bayesian decision-making principle, combined with the Bayesian cell ant colony algorithm, the Bayesian prior probability and the posterior probability are calculated, the flight path of the UAV is planned, and the optimal path is obtained. The experimental results show that the obstacle avoidance signal captured by the method encounters static obstacles fluctuates in the range of -28-30mV, and the obstacle avoidance signal captured by dynamic obstacles fluctuates in the range of -27-30mV, which is consistent with the actual obstacle signal fluctuation range. , The obstacle avoidance effect is better.