The fast development of the brain-inspired neuromorphic computing system has ignited an urgent demand for artificial synapses with low power consumption. In this work, it is the first time a light-stimulated low-power synaptic device based on a single GaN nanowire has been demonstrated successfully. In such an artificial synaptic device, the incident light, the electrodes, and the light-generated carriers play the roles of action potential, presynaptic/postsynaptic membrane, and neurotransmitter in a biological synapse, respectively. Compared to those of other synaptic devices based on GaN materials, the energy consumption of the single-GaN-nanowire synaptic device can be reduced by more than 92%, reaching only 2.72×10-12J. It is proposed that the oxygen element can contribute to the synaptic characteristics by taking the place of the nitrogen site. Moreover, it is found that the dynamic “learning-forgetting” performance of the artificial synapse can resemble the behavior of the human brain, where less time is required to relearn the missing information previously memorized and the memories can be strengthened after relearning. Based on the experimental conductance for long-term potentiation (LTP) and long-term depression (LTD), the simulated network can achieve a high recognition rate up to 90% after only three training epochs. Such few training times can reduce the energy consumption in the supervised learning processes substantially. Therefore, this work paves an effective way for developing single-nanowire-based synapses in the fields of artificial intelligence systems and neuromorphic computing technology requiring low-power consumption.