Spatio-temporal prediction task is widely applied in neuroscience， transportation， meteorology and other fields. As a typical spatio-temporal prediction task， temperature prediction needs to dig out the inherent spatio-temporal characteristics of temperature data. Aiming at the problems of large prediction error and insufficient spatial feature extraction in the existing temperature prediction algorithms， a temperature prediction model based on Graph Convolutional Network and Gated Recurrent Unit （GCN-GRU） was proposed. Firstly， the methods of weight redistribution and multi-order neighbor connection were used to modify Graph Convolutional Network （GCN） in order to effectively mine the unique spatial characteristics of the meteorological data. Secondly， the matrix multiplication of each recurrent unit in the Gated Recurrent Unit （GRU） was replaced by graph convolution operation， and all the recurrent units were connected in series to form a graph convolutional gating layer. Then， the graph convolutional gating layer was used to build the main network structure to extract the spatio-temporal characteristics of the data. Finally， the temperature prediction results were output through a fully connected output layer. Compared with the single models such as Gated Recurrent Unit （GRU） and Long Short-Term Memory （LSTM）， GCN-GRU had the Mean Absolute Error （MAE）reduced by 0.67 and 0.83 respectively； compared with the prediction model combined with Chebyshev graph convolution and Long Short-Term Memory （Cheb-LSTM） and the prediction model combined with Graph Convolutional Network and Long Short-Term Memory （GCN-LSTM）， the proposed model had the MAE reduced by 0.36 and 0.23 respectively.