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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)