Simulation of Tourist Attraction Traffic Prediction Model Based on Support Vector Machine Algorithm

With the rapid growth of China's economy, the pressure on people's work and life is increasing, and the demand for relief through tourism is becoming increasingly urgent. The contradiction between scenic resources and people's tourism needs is also becoming increasingly acute. Passenger flow prediction can compensate for the impact of strong cyclical and fluctuating passenger flow shocks on scenic spots and tourists, enabling limited tourism resources to be reasonably scheduled and allocated in advance. Traditional passenger flow data prediction models obtain high dimensionality data, making it difficult to eliminate redundant data, resulting in significant prediction errors. In order to enhance the scientific management of scenic spots, alleviate traffic pressure, reduce safety hazards, and improve tourist experience, this paper proposes a tourist attraction flow prediction model based on Support Vector Machine (SVM) algorithm. The model collects historical data of tourist flow from the Internet, constructs a training set for tourist flow prediction based on embedding dimension and delay time. The SVM algorithm is used to learn the training set for tourist flow in the scenic spot, and establishes a tourist flow prediction model for the scenic spot, Finally, analyze the superiority of the model. The experimental results indicate that the proposed method has higher prediction accuracy and good application value in predicting tourism passenger flow.