A model which is capable of capturing the spectral distribution features helps to improve the WDMS(White Dwarf + M Sequence Binaries) classification system running in SDSS-DR10 because the distribution feature of a spectra is one of the most important factors that determine its spectral type. Radial basis function (RBF) neural network is an efficient computational model that is widely used for numerical approximation and object classification. However, due to the reason that the network’s hyper-parameters are usually determined empirically, the performance of the network is limited. In this paper, on the basis of analyzing the distribution features of WDMS in a high dimensional space, an automatic classification model for WDMS ia propose based on RBF neural network. And according to the features, we propose centroids criterion and width criterion to determine hyper-parameters for the network in a more theoretical way, which improves the accuracy of the model. After training and applying the model, a total number of 4 631 WDMS candidates are classified and 25 of them are newly identified, which proves the feasibility of the model and provides further researches on WDMS with more data.