This paper applies deep learning method on establishing a model to discriminate the precipitation type. Hundreds of thousands of precipitation samples obtained from sounding and observation data of China from 1996 to 2015 were divided into rain and snow events. The 2016-2017 data were tested, and a case test was conducted on a rain and snow weather process over China in late January 2018. Furthermore, the application of deep learning method to discrimination and forecasting of precipitation type was discussed. The main conclusions are as follows. Discrimination accuracy of the model is 98.2%, which is more improved than the traditional index threshold method. TS scores of rain and snow are 97.4% and 94.4%, false discriminate rates are 1.7% and 2.0%, and omission rates are 1.0% and 3.7%, respectively. The case test denotes that the model discrimination results based on the observation data are basically consistent with the observation data. The ECMWF precipitation type products and the model results also have a good forecast performance for precipitation type over China. Compared with ECMWF, the model forecast for rain and snow separating line is more consistent with observation. The test results show that the discrimination model established in this paper can extract the key features of precipitation type of rain and snow. The application of deep learning method to discrimination and forecasting of precipitation type is feasible and advantageous. Thus, this method could provide important technical support for the objective identification and prediction of precipitation type.