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For the characteristics of Regional traffic (Strong volatility, Complex nonlinearity, Susceptible to seasonal effects), as the single neural network model cannot learn temporal and spatial correlation problems simultaneously. By analyzing the influencing factors of regional tourist flow, combining residual networks with fully connected networks. The author proposes an improved Quad-ResNet model for regional tourist traffic forecasting. The Quad-ResNet model integrates 4 residual networks and a fully connected network, in learning spatial correlation through deep convolution, learning time proximity, similarity, periodicity and trend by combining 4 residual networks, also learning seasonal influencing factors by using a fully connected network. Comparing the Quad-ResNet model with the LSTM, CNN, and ST-ResNet models on the same data set for regional tourist traffic forecasting, the experimental results demonstrate that the deviation of Quad-ResNet model is smaller than other models. Moreover, it is obviously easier to train and forecast than the LSTM model, is more suitable for regional tourist traffic forecasting.