Disclosed is a convolutional neural network based multi-point regression forecasting model for traffic flow forecasting, comprising the following steps: a first perception input layer and a second convolutional layer: performing convolution on data of the input layer and outputting after passing an activation function; a plurality of convolutional layers: using the output of the previous layer as an input to perform convolutional processing and outputting after passing an activation function; a fourth all-link layer and a fifth dropping layer: "a random dropping layer" discarding some redundant nerve cells and maintaining 40-70% of all-link nodes of the previous layer; and a sixth output layer: performing regression calculation on an effective node output of the dropping layer; the obtained regression numerical value being an output of the entire network; setting m output nodes, that is, mapping the all-link layer to the output layer as a weight combination. Compared with the traditional statistical regression model, the regression forecasting model has data space associated feature extraction capability, has the advantages of local perception and weight sharing, and has good balance on time complexity and feature selection.
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