Aiming at the problem that the existing efficiency prediction methods are difficult to reflect the actual effectiveness of anti-missile equipment system， a method of efficiency evaluation of anti-missile equipment system based on "data-driven + deep learning" is proposed. On the basis of a large number of experimental data extraction， disposal and analysis， we construct grey wolf optimization （GWO）-deep belief network（DBN） model to train the data， so as to obtain the nonlinear fitting of the anti-missile equipment system efficiency. We conduct a simulation experiment with an anti-missile system efficiency evaluation as an example， and the results show that the evaluation method is feasible and reliable. It can provide high reference value and significance for the demonstration and improvement of the anti-missile equipment system.

As an important link in the terminal high-and-low-level anti-missile operations， the pre-war optimal allocation of equipment resources directly determines the lower limit of anti-missile combat effectiveness. It also greatly increases the upper limit of anti-missile combat effectiveness. In the face of complex and changeable air and space threats， the resources of anti-missile equipment are always limited. How to intercept incoming targets through configuration optimization is of great research significance. The concept of defense benefits is proposed and the terminal anti-missile equipment resource allocation model is established by taking the incoming ballistic missiles as imaginary scenario，and considering the defense targets and the effectiveness of anti-missile weapons and equipment，The specific models are solved by using the Beetle Antennae Search （BAS）. The optimized configuration plan of the specific calculation example is obtained， which has certain reference significance for the pre-war planning of the regional anti-missile warfare.