BP (Back Propagation) neural network and PSO (Particle Swarm Optimization) are two main heu- ristic optimization methods， and are usually used as nonlinear inversion methods in geophysics． The authors ap- plied BP neural network and BP neural network optimized with PSO into the inversion of 3D density interface re- spectively，and a comparison was drawn to demonstrate the inversion results． To start with，a synthetic density interface model was created and we used the proceeding inversion methods to test their effectiveness． And then two methods were applied into the inversion of the depth of Moho interface． According to the results，it is clear to find that the application effect of PSO- BP is better than that of BP network． The BP network structures used in both synthetic and field data are consistent in order to obtain preferable inversion results． The applications in synthetic and field tests demonstrate that PSO- BP is a fast and effective method in the inversion of 3D density interface and the optimization effect is evident compared with BP neural network merely，and thus，this method has practical value．