  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 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.
