In order to more accurately predict the pedestrian flow and understand the interactive relationship between tourist space and pedestrians, this paper uses spatial syntax and neural network methods to construct an evaluation model of tourist road congestion. This model makes full use of the advantages of neural network method and spatial syntax. For example, neural network method can objectively and dynamically assign the weight of attractions, and it can estimate the weight of other attractions

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... through training. Analysis, we can clearly understand the connection relationship between roads; then we use mathematical formulas to effectively combine the road network structure and landscape attractions, which can correspond to the street network structure, the distribution of attractions and pedestrian movement The ability to estimate road congestion in low and inconsistent situations. We experimented with Gulangyu Island in Xiamen. As a result, we found that 1.the attractions of Gulangyu Island are mainly located on the edge of the island, and the attraction of several attractions that sell tickets reaches above 0.9; 2.The topological model of spatial syntax can better predict the walking results of tourists in Gulangyu Island; 3.The road accessibility and the distribution of scenic spots in Gulangyu Island have no great spatial correlation, but the model can predict the degree of road congestion To bring it closer to the truth. The results of our research can be used as a basis for future tourism space management and can enrich the research of tourism space.