Taking Beijing as an example and using the data of air quality monitoring stations from 2015 to 2018, the impacts of temporal resolution and spatial information on the PM_2.5 concentration prediction were analyzed by a BP neural network, an LSTM network, and a CNN-LSTM hybrid model. The results show that neural network models are generally better than the multi-linear regression model. Increasing the temporal resolution of the input data can significantly improve the accuracy of the predicted daily average PM_2.5 concentration. When the temporal resolution of the input data increases from one day to 6 hours, the mean absolute error of the LSTM model reduces from 27.39 μg/m³ to 20.59 μg/m³. This improvement is more obvious when the weather is significantly getting better or getting worse. The distribution of PM_2.5 concentration in North China has distinct spatial and temporal characteristics. The first spatial mode is a uniformly increasing or decreasing mode, and the second one is a north/south dipole mode. The analysis shows that the concentration of PM_2.5 in Beijing is related to the PM_2.5 in Inner Mongolia, Hebei, and Tianjin of the previous day. The CNN-LSTM hybrid model, trained with the spatialtemporal information of PM_2.5 in North China, can further improve the predictability of PM_2.5 in Beijing. It further reduces the mean absolute error to 17.36 μg/m³.