This paper proposes a deep Q network (DQN)-based solution framework to minimize UAV’s energy consumption in UAV-assisted wireless powered sensor network under the age of information (AoI) constraint, where a UAV wirelessly charges ground sensors and then the sensors use harvested energy to upload their freshly collected information to the UAV. The corresponding non-convex energy-minimization problem is first modeled as a Markov process, and then the state spaces, action spaces and reward function are designed. Simulation results show that the proposed DQN achieves much smaller energy consumption than traditional greedy-based scheme, and when the number of sensors is more than 8, traditional greedy-based scheme becomes very difficult to solve the problem, while our presented DQN method can still find an optimal solution. Moreover, the UAV’s energy consumption increases with the decrease of AoI or the increment of sensors’ amount, and with the rotation angle constraint, UAV’s trajectory becomes smooth.