In order to study the spatial and temporal distribution of visitor flow in the construction of tourism culture, this article first studies and analyzes the space-time behavior of tourists, and divides the environment of the scenic spot into three parts: entrance, exit, and stop. Then, based on the three parts, the time and space distribution data of tourists are collected from five aspects: the arrival probability distribution of tourists, the probability of transition between tourist attractions, the distribution of attraction time, the moving time between attractions, and the area of scenic spots. The number of visitors in each attraction, and by fitting the curve, the probability distribution of the tourist time of each attraction is obtained. Finally, a neural network–based prediction model of the space-time distribution of tourists is established. The collected data is brought into the neural network. By comparing the predicted values with the actual values, the model has high prediction accuracy and can be used to predict the spatial and temporal distribution of tourists.

中文翻译：

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基于反向传播神经网络模型的旅游文化建设中游客流的时空分布模型

为了研究旅游文化建设中游客流的时空分布，本文首先对游客的时空行为进行了研究和分析，将风景名胜区的环境分为出入口，出入口和出入口三部分。停。然后，基于这三个部分，从五个方面收集游客的时空分布数据：游客的到达概率分布，景点之间的过渡概率，景点时间的分布，景点之间的移动时间以及风景名胜区。每个景点的游客人数，并通过拟合曲线，获得每个景点的游客时间的概率分布。最后，建立了基于神经网络的游客时空分布预测模型。收集的数据被带入神经网络。通过将预测值与实际值进行比较，该模型具有较高的预测精度，可用于预测游客的时空分布。