Research On Amusement Park Operation Analysis Based On Multi-source Data

With the improvement of people's consumption level,domestic and international tourism culture is prevalent.According to the National Bureau of Statistics,the total number of domestic tourists reached 5 billion in 2017,an increase of 12.6% compared with 2016,and the number of domestic tourists has increased year by year in the past decade.The amusement park is a leader in the tourism industry as an entertainment venue for all ages.However,with the development and development of the domestic and international tourism market,various new types of playgrounds have emerged,and the amusement park industry has also been threatened and challenged.In the era of big data,the traditional operational analysis model can no longer meet the needs of the sustainable development of amusement parks.Only by making multiple changes and innovations that adapt to the times can we stand out in the multi-party competition.Supported by new information technologies such as the Internet of Things and mobile communications,amusement parks have been able to collect data from daily operations,entertainment devices,consumer behavior and external influences,but they are often distributed in amusement parks.The department lacks comprehensive and systematic analysis of the above data and cannot fully exploit the actual value of these data.The existing research lacks a comprehensive analysis of the operational analysis of amusement parks from a system perspective.Therefore,this paper firstly analyzes the needs of the amusement park industry,managers and tourists.Then,based on this,the design of the amusement park operation analysis index system based on multi-source data is designed.Integrate real-time operational data related to tourists,equipment,sales,etc.,and analyze the associations and rules behind the multi-source data of amusement parks.At the same time,the paper also uses Tableau software to construct the operational analysis prototype system,and analyze the main operational analysis indicators of amusement parks.The changing laws and trends are visualized,and the operation status of the park is visually reflected from the macro and micro levels.Finally,the traditional regression prediction model and three data mining prediction models—SVR model,decision tree regression model and BP neural network model For comparison,an effective model for passenger traffic forecasting in amusement parks is analyzed.The system helps managers to analyze the current situation and future predictions of amusement park operations from multiple angles and deep levels,thus providing a scientific reference for enterprise management decision-making optimization.