## 3 Case Application and Analysis
The reason is that the traffic load of the node is composed of the traffic load of the road connected to the node, and the weighted connection degree is the number of road segments connected to the node, so the two have a high correlation. The correlation between other indicators was low, which indicates that these indicators reflect the attribute information of road network nodes from different aspects, and the importance of a single evaluation index to evaluate the importance of nodes has limitations. Hence, we should comprehensively consider the characteristic indicators of nodes for systematic analysis of the importance of nodes. Therefore, the dynamic classification method was used to grade the nodes according to the three attribute indexes of nodes, as shown in Figure 1:6 The spatial distribution of the node categories of the whole road network was shown in Figure 1:7  
Figure 1:8 Clustering results of nodes. Figure 11 shows that the Grade I nodes (red nodes) were mostly distributed in the core layer of the road network. The Grade II nodes (brown nodes) were generally distributed in the outer circle layer of the road network. The Grade III nodes (green nodes) were evenly distributed in the road network space. However, the node classification result identified by the dynamic classification method is only a dynamic clustering based on the similarity between node attributes, without taking into account the impact of the comprehensive importance of nodes, the classification and recognition results may be biased. Therefore, based on the objective weighting method of information entropy, the weight vector of evaluation indexes was calculated as \(\mathbf{w}=(0.30033,0.34876,0.35091)\), and the comprehensive importance of each node was calculated by the TOPSIS method, as shown in Figure 1:9  
The three-grade classification of road network nodes was obtained according to the dynamic classification method (DT). Then the comprehensive evaluation value of the road network node is obtained by using the multi-attribute comprehensive evaluation method, and the exact threshold values of a comprehensive evaluation for assigning levels to nodes are obtained according to the clustering center of the dynamic classification result. Comprehensive consideration of the importance ranking and dynamic hierarchical clustering of nodes, it was determined that the nodes with comprehensive evaluation value \(Z_{i}\geq 0.45\) were the first-grade nodes, the nodes with \(0.45>Z_{i}\geq 0.30\) were the secondary  
Figure 3:0 Spatial distribution of node classification. Figure 3:1 Overall importance values of nodes. grade nodes, the nodes with \(Z_{i}\) < 0.30 were the third-grade nodes. Finally, there were 20 first-grade nodes, accounting for 17.70%; 52 secondary-grade nodes, accounting for 46.02%; 41 third-grade nodes, accounting for 36.28%, as shown in Figure 3:2 The spatial distribution characteristics of each category of nodes were clearly listed in Figure 3:3 The first-grade nodes (red marking) were regarded as the key node of the road network, the second-grade nodes were yellow marking nodes, and the third-grade nodes were white marking nodes. The first-grade nodes identified were the intersections between expressways and trunk roads, trunk road, and trunk road. The importance of Node 5 was the least and the comprehensive evaluation value was 0.4577. The self-organizing competitive neural network method in literature [15] is a deep learning method, which builds a network prediction model according to the internal correlation of the evaluation index data, and classifies nodes by the trained network model. The method in this paper is compared with the existing methods, which has verified the feasibility and effectiveness of the method in this paper. According to the internal relationship between the index data of road network nodes, the dynamic classification method forms the clustering group of road network nodes, that is, the hierarchical classification of road network nodes.