Unmanned aerial vehicles (UAVs) are increasingly considered in safe autonomous naviga-tion systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings.However,how to achieve UAV-enabled data dissemination and also ensure safe nav-igation synchronously is a new challenge.In this pa-per,our goal is minimizing the whole weighted sum of the UAV\'s task completion time while satisfying the data transmission task requirement and the UAV\'s fea-sible flight region constraints.However,it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate sys-tem model in practice.To overcome this tough issue,we propose a new solution approach by utilizing the most advanced dueling double deep Q network (duel-ing DDQN) with multi-step learning.Specifically,to improve the algorithm,the extra labels are added to the primitive states.Simulation results indicate the va-lidity and performance superiority of the proposed al-gorithm under different data thresholds compared with two other benchmarks.