The application relates to an intelligent metasurface security transmission optimization method based on a supervised deep neural network. The method comprises the following steps: acquiring a
signal of a transmitting integrated RIS, modeling an RIS-to-legal user channel, combining the
signal and the channel, and calculating a legal user received
signal-to-interference-and-
noise ratio and an achievable rate; meanwhile, corresponding indexes at an eavesdropper and an achievable rate of an
eavesdropping link are calculated. Then, a
multicast system security capacity is designed according to a minimum value of the achievable rate of the legal user and the achievable rate of the
eavesdropping link. In order to achieve the target, an optimization model is constructed by combining the achievable rate of the legal user, RIS power and a perceived signal-to-
noise ratio constraint. A supervised deep neural network and a
training set are constructed, an offline training, online
inference and parameter updating are carried out by using an Adam optimizer according to a preset
loss function, and a trained network is obtained. Finally, the optimization model is solved by using the network, and RIS phase shift and power allocation coefficients are acquired. The method can reduce the calculation complexity and improve the real-time performance.