In order to improve the hit accuracy of TV terminal guidance projectile to the ground target, reduce the falling angle error of the projectile, and realize the goal of hitting the target at a desired falling angle, a sliding mode guidance law combining means clustering with RBF neural network is proposed, for which an analysis is made to characteristics of sliding mode guidance law with falling angle constraint, and the shortage of Radial Basis Function (RBF) neural network sliding mode guidance law that is difficult to hit the target at the desired falling angle is taken into consideration.The new guidance law enables the neural network to adjust the clustering center and center value continuously according to the real-time flight state of the projectile in the learning process, and make the center value be always the optimal solution in the current flight state, thus to realize the optimization of guidance law.Numerical simulation is made to static target and moving target.The results show that, compared with sliding mode control with falling angle constraint and RBF neural network sliding mode control, the proposed guidance law can make the projectile hit the target at the desired falling angle, and is more robust.