Abstract: A traffic flow forecasting method based on Deep graph Gaussian processes includes: S1, with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an Aggregation Gaussian process to extract dynamic spatial features; S2, obtaining a Temporal convolutional Gaussian process from weights at different times and a convolution function that obeys the Gaussian processes, and obtaining temporal features in traffic data by combining the Aggregation Gaussian process; S3, constructing a Deep graph Gaussian process method integrating a Gaussian process and a depth structure from the Aggregation Gaussian process, the Temporal convolutional Gaussian process and a Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the Deep graph Gaussian process method to obtain a forecasted result.