In order to shorten the fussy experimental process in synthesizing polydiphenysilylenemethylene (PDPhSM) technology, a back propagation (BP) neural network model and a radial basis function (RBF) neural network model are developed to approach the complex nonlinear relationship between technology parameters and polymerization efficiency for synthesizing PDPhSM matrix nanocomposite thin film respectively. By using the constructed neural network model, the relationship between the technology parameters (laser fluence, ambient pressure, distance between target and substrate, deposition time) and polymerization efficiency is discussed, and the weakness that the nonlinear relationship could not be approached more accurately, effectively by using of single-factor-experiment method is overcomed. Predicted and test results showed that all the relative errors between the desired values and predicted outputs of the network are less than 10%, but the predicted data of RBF model are well acceptable when comparing them to the real test values, hence providing a effective, economical way for synthesizing PDPhSM matrix nanocomposite thin film.