The large number of distributed photovoltaic (PV) access on the user side increases the difficulty of net load forecasting. In order to improve the forecasting accuracy, a short-term net load probabilistic forecasting model based on weather classification and neural network is proposed. First of all, in order to better describe the meteorological conditions, the weighted sum of cloud cover and ambient temperature that have the greatest influence on PV output is carried out to construct a comprehensive meteorological impact factor, and a weather classification method based on the volatility of meteorological impact factor is proposed. At the same time, the maximum information coefficient is used to help the model select the input characteristics in different time frames in view of the time difference of the influence of meteorological factors on the net load. Finally, using the sample selection results as input, probability prediction is made by quantile regression and gated loop unit (QR-GRU) neural network, and probability density curve is generated by kernel density estimation. The simulation results show that the model has good prediction performance.