Artificial neural networks have been developed and widely applied in computer vision and brain-like intelligence. In the past decades, research on neural networks focuses on higher accuracy rates but neglects the control of network computational costs. The human brain, as an efficient and energy-saving network, plays an important role in the development of artificial intelligence. How to emulate the connectivity properties of biological brain networks and build an ultra-low energy artificial neural network model for achieving essentially the same correct target recognition rate has become a hot research topic. To build an ultra-low artificial neural network model, this study realizes network efficiency by combining the connection properties of brain networks to change the connections of artificial neural networks. The experimental results show that combining the connectivity properties of biological brain networks to change the connections of the networks largely reduces the computational cost of the network, while the performance of the network is not significantly affected.