###### Abstract
Usually, the factors considered for the route selection scheme include population flow, environmental protection assessment, geological survey, project cost, and station route selection [13], etc., and the comprehensive route selection research method is used to select among many control factors through technical and economic comparison [14]. Moreover, the balance of interests to choose a suitable route that can meet the interests of all parties. For example, literatures [15; 16] proposed that biodiversity is an important aspect of protecting the ecological environment. Routes that affect the development of ecosystems and biodiversity are therefore rejected during environmental impact assessments during development site selection. In [17], a geographic information system (GIS)-based network analysis and analytic hierarchy process (AHP) approach created a new hybrid route considering economic and environmental criteria and it was compared with three different routes from pre-construction studies. These traditional methods usually only complete the first step for route planning. Most of them expounded the objective influencing factors on route selection, but did not formulate a reasonable route selection strategy. Moreover, in many practical situations, this kind of evaluation index can only filter out the general route of travel, but no in-depth research has been carried out on the specific cities or stations to be passed. Numerous studies have shown that GNN's excellent ability to deal with unstructured data has made new breakthroughs in data analysis, recommendation systems, physical modeling, natural language processing, and graph combination optimization problems. Therefore, it can also be applied to site prediction of traditional high-speed rail networks. However, the number of high-speed railway lines in the eastern region of China far exceeds that in the western region. Analogous to the network on the graph, the paper defines this line structure as an edge graph structure network. Among them, the edge graph structure refers to the existence of some edge nodes in the graph, that is, the unbalanced structure formed by the degree of some nodes in the graph is much smaller than other nodes. This indicates that some sites in the network of high-speed rail lines have much larger degrees than others. For example, Beijing station has far more adjacent stations than Bijie station. In the case of extremely unbalanced site degree, directly training the GNN classifier makes the vector representation ability of the site with more neighbors stronger, and the loss function calculation is accurate. The sites with fewer neighbors are marginalized, and the vector representation capabilities of these edge sites are relatively insufficient, and the loss calculation error is large. For edge sites, their neighbors are much more important than those of large sites, and the neighbors of edge sites are always under-sampled compared with other sites during the vector aggregation process, which leads to suboptimal prediction performance. In order to make up for the shortcomings of the above research methods, this paper uses the graph neural network [18; 19; 20; 21; 22; 23] to model the main national high-speed railway line network, aiming at predicting the passing stations of the Yinchuan-Chongqing high-speed railway line. The model first uses the Node2 vec algorithm to generate the vector representation of each high-speed rail station, and then proposes an adaptive function to effectively identify the edge stations in the high-speed railway network, and then uses the graph attention mechanism [20] to complete the potential neighbors of the edge stations. This ensures that during the embedding process of each site, the difference in the number of their neighbor sites is as small as possible. Finally, the connection probability between possible sites is calculated. It should be noted that due to the extremely limited number of railway stations, the prediction effect of the graph deep learning model has certain limitations.