The existing backoff mechanism of the statistic priority-based multiple access (SPMA) protocol relies on static function models and has single dimension of optimization parameters, making it unable to adapt to dynamic transmission and multi-priority requirements in UAV ad hoc networks. To address this issue, the dynamic decision-making process of node selection for backoff time in the SPMA protocol was modeled as a Markov decision process, and an intelligent backoff strategy based on the double deep Q-network (DDQN) was innovatively proposed. This strategy comprehensively considered factors such as service priority, thresholds, and channel load, and adopts the DDQN algorithm to select backoff time within a finite, discrete action space. Simulation results show that, compared to traditional binary exponential backoff strategies and logarithmic function-based backoff strategies, the proposed strategy can reduce the transmission delay for low-priority services by up to 33.3%, increase the initial backoff success rate by 18%, and effectively improve the transmission success rate and adapt to the variation of network scale well.