Traffic flow prediction for urban road network is influenced by historical traffic flow and traffic flow at adjacent intersections， which has complex spatio-temporal correlation. For the lack of correlation analysis on traffic flow data， capturing small changes but ignoring long-term time characteristics in traditional spatio-temporal residual models， a short-term traffic flow prediction model for urban road network based on improved spatio-temporal residual Convolutional Neural Network （CNN） was proposed. In the proposed model， the original traffic flow data was transformed into traffic grid data， and Pearson Correlation Coefficient （PCC） was used to analyze the correlation of traffic grid data， so as to determine the periodic series and adjacent series with high correlation. At the same time， the periodic series model and the adjacent series model were established， and Long Short-Term Memory （LSTM） network was introduced as the hybrid model to extract the time characteristics and capture the long-term time characteristics of the two series. Experimental results on Chengdu taxi dataset show that the proposed model can predict traffic flow better than benchmark models of LSTM， CNN and the traditional residual model. When the evaluation index is Root Mean Square Error （RMSE）， the average prediction accuracy of traffic road network in the test set is improved by 25.6%， 13.3% and 3.2% respectively.