Abstract

With the vigorous development of sports, people’s awareness of engaging in sports has gradually increased, and the requirements for a sports culture center have been higher. However, the service system of traditional sports cultures center is single, which cannot meet people’s growing experience needs. Therefore, it is urgent for the service system of sports culture centers to move towards intellectualization. Firstly, this paper discusses the service system of traditional sports culture centers and finds that there are some problems, such as slow transmission of information, poor sharing of resources, and weak flexibility of response, which seriously affect the consumer experience of users and restrict the development of sports culture centers. Then, with the help of computer network technology, the design of intelligent system architecture of sports culture centers is completed, which makes many intelligent subsystems interconnected and interoperable, integrates information, realizes the integration of data application network, and achieves the goal of resource sharing and function upgrading. Then, based on the intelligent system, the big data platform is built with the help of big data technology, and the support vector machine-back propagation (SVM-BP) neural network composite model is used to realize the prediction of the passenger flow in the cultural center, which provides guidance for adjusting the service plan in advance, effectively coping with the peak passenger flow and improving the user experience. Finally, through empirical analysis, we know that the design of an intelligent system greatly improves the service quality of cultural centers. The research results not only achieve a significant increase in passenger flow but also provide an effective way for the service of sports culture centers to move towards intellectualization.

Highlights

  • The opening and service of sports and cultural centers provide people with sports convenience

  • Aiming at the design of the intelligent system of sports culture centers, Liang H T [4] designs an intelligent stadium system to improve the efficiency and economic benefits of intelligent management based on the requirements of the system design of Internet of Things technology and the overall structure, system functions, and Internet of Things architecture of the system

  • The big data technology is used to extract and classify the passenger flow information data of cultural centers intelligently, and the support vector machine-back propagation (SVM-BP) neural network composite model based on machine learning is used to predict the passenger flow dynamically in real time, which realizes the intelligent feedback of the system and the timely formulation of the scheme, provides theoretical guidance for the intellectualized construction of the sports culture center, and has great significance for improving the service efficiency and customer satisfaction of the sports culture center

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Summary

Introduction

The opening and service of sports and cultural centers provide people with sports convenience. The subsystem realizes data sharing through information interconnection and interoperability to provide users with humanized intelligent services On this basis, the big data technology is used to extract and classify the passenger flow information data of cultural centers intelligently, and the support vector machine-back propagation (SVM-BP) neural network composite model based on machine learning is used to predict the passenger flow dynamically in real time, which realizes the intelligent feedback of the system and the timely formulation of the scheme, provides theoretical guidance for the intellectualized construction of the sports culture center, and has great significance for improving the service efficiency and customer satisfaction of the sports culture center

Systematic analysis of traditional sports culture centers
Analysis of service ability index of sports culture centers
Big data technology
Intelligent monitoring system
Communication network system
Event service subsystem
Building a big data platform
Improvement of passenger flow forecast model
Example analysis
Conclusion
Full Text
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