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In order to more accurately predict the pedestrian flow and understand theinteractive relationship between tourist space and pedestrians, this paper usesspatial syntax and neural network methods to construct an evaluation model oftourist road congestion. This model makes full use of the advantages of neuralnetwork method and spatial syntax. For example, neural network method canobjectively and dynamically assign the weight of attractions, and it canestimate the weight of other attractions through training. Analysis, we canclearly understand the connection relationship between roads; then we usemathematical formulas to effectively combine the road network structure andlandscape attractions, which can correspond to the street network structure,the distribution of attractions and pedestrian movement The ability to estimateroad congestion in low and inconsistent situations. We experimented withGulangyu Island in Xiamen. As a result, we found that 1.the attractions ofGulangyu Island are mainly located on the edge of the island, and theattraction of several attractions that sell tickets reaches above 0.9; 2.Thetopological model of spatial syntax can better predict the walking results oftourists in Gulangyu Island; 3.The road accessibility and the distribution ofscenic spots in Gulangyu Island have no great spatial correlation, but themodel can predict the degree of road congestion To bring it closer to thetruth. The results of our research can be used as a basis for future tourismspace management and can enrich the research of tourism space.

Research on Evaluation Model of Road Congestion of Tourist Attraction  Based on Spatial Syntax and Neural Network Method -- A Case of Gulangyu  Island,Xiamen,China