The rapid growth of passenger flows has brought a series of challenges to the environment and safety management of tourist attractions. It is vital to establish an accurate passenger flow prediction model to reduce the risks associated with human flows. Owing to the limitation of a single data source, the existing research on the prediction of tourist flows in scenic spots ignores the impact of public transport passengers on the internal tourist flow in scenic areas. The prediction model lacks the learning process of data samples, and the ability of generalization and self-study is weak. In this paper, we propose a new method of predicting passenger flow in scenic areas based on a convolutional neural network and long short-term memory (CNN-LSTM) hybrid neural network (HNN) model, which considers the multisource traffic flow around a scenic area. It uses a series of HNNs to mine the temporal and spatial correlation between the passenger flows from multiple sources and solves the problem of data stability dependence. The time series of the passenger flow in the study area was designed and extracted on the basis of the spatial analysis of South Luogu Lane in Beijing, and the input structure was constructed by combining the multisource traffic passenger flow dimension. This model for predicting passenger flow in scenic areas based on CNN-LSTM provides a reference for the comprehensive application of multisource data in scenic areas and has high accuracy and robustness.