The graph neural network model has a wide range of application value in the intelligent transportation system. However, the data design pattern based on graph structure cannot solve the directional and hierarchical problems of node information transmission. This makes the deep models represented by graph convolutional networks lack a certain predictive ability in node distribution scenarios. In this study, a tree spatial-temporal model with tree structure as the sample space is designed for the traffic node distribution scenarios. Firstly, road nodes and spatial relationships are abstracted according to the graph structure, so as to realize the preliminary spatial distribution relationship of nodes. Secondly, different nodes are used as the root nodes of the tree to construct the plane tree structure and plane tree matrix to complete the conversion process from the graph structure to the tree structure. Finally, the plane tree matrices of all nodes are fused into a spatial tree matrix representing the spatial global relationship of the nodes. This study designs the deep tree traffic forecast model based on tree structure, which converts the graph structure of small-scale aggregated nodes into tree structure. The deep tree traffic forecast model realizes the mining and prediction tasks of various traffic measurements based on the spatial tree convolution module and the temporal convolution module. This study demonstrates the excellent predictive ability of the deep tree traffic forecast model in traffic node distribution scenarios by comparing with multiple existing baselines on real datasets.