Traffic Forecast and Business Operation Optimization Strategy of Smart Tourist Attractions Driven by Big Data

In order to improve the competitiveness of smart tourist attractions in the tourism market, this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot. Firstly, this paper introduces the big data-driven forecast model of scenic spot passenger flow. Based on the traditional autoregressive integral moving average model and artificial neural network model, it builds a big data analysis and forecast model. Through the analysis of data source, model building, scenic spot passenger flow accuracy, and modeling time comparison, it affirms the advantages of big data analysis in forecasting scenic spot passenger flow. Finally, it puts forward four commercial operation optimization strategies: adjusting the ticket pricing of scenic spots, upgrading the catering and accommodation services in scenic spots, planning and designing play projects, and formulating accurate scenic spot marketing strategies, in order to provide references for the optimization and upgrading of smart tourist attractions in the future.