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Power consumption is the main limiting factor of network on chip (NOC). The selective on/off switching algorithm of link state can reduce the link power consumption at circuit level and system level. Most of these algorithms focus on a simple static threshold trigger mechanism, which determines whether the link should be turned on or off. In order to solve many limitations of the above trigger mechanism, an artificial neural network (ANN) for NOC is proposed as a dynamic link power consumption management method. This method is based on supervised online learning of system state, and uses a small scalable neural network to close and open the link, so as to improve the prediction ability. The model based on artificial neural network makes use of very low hardware resources and can be integrated in large mesh and torus NOC. The simulation results of various comprehensive traffic models on different network topologies show that compared with static threshold calculation, this method can save power consumption with low hardware overhead. It can provide ideas for solving the power consumption problem in link management NOC.