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Neutron-gamma discrimination is a tough and significative in experimental neutrons measurements procedure, especially for low-energy neutrons signal discrimination. In this work, based on the Pulse Shape Discrimination (PSD) and Back-Propagation (BP) artificial neural networks, a neutron-gamma discrimination method is developed to broaden the lower limit of energy threshold with the hidden layer of 20 neurons. Compared with neutron-gamma discrimination method based on PSD only, the developed neutron-gamma discrimination method based on the PSD and BP-ANN can discriminate neutron and gamma-ray signals with low energy threshold, which can discriminate signals up to 99.93%. Moreover, this work can reduce the energy threshold from 350 keV to 70 keV, as well as the acquired data utilization increased from 60% to more than 99.9%, which overcome the hardware limitations and distinguish neutron and gamma-ray signals, effectively. The developed neutron-gamma discrimination method and the trained neural network can be directly used to other experimental neutrons measurements. • Based on the Pulse Shape Discrimination (PSD) and Back-Propagation (BP) artificial neural networks, a neutron-gamma discrimination method is developed to broaden the lower limit of energy threshold from 350 keV to 70 keV, and the acquired data utilization increases from 60% to more than 99.9%. • Compared with neutron-gamma discrimination method based on PSD only, the developed neutron-gamma discrimination method based on the PSD and BP-ANN can discriminate neutrons and gamma rays with low energy threshold, which can discriminate signals up to 99.93%. • In experiment, the inverse-time-of-flight spectroscopy technology is introduced to verify reliability of the developed neutron-gamma discrimination method. • The experiment verify that the trained neural network can also be used to other experimental measurements for neutron-gamma discrimination with low energy threshold. [ABSTRACT FROM AUTHOR]