Multi-source data fusion analysis model for intelligent operation of stadiums

In order to solve the problems of data dispersion, information silos and inefficient management in the operation of traditional stadiums, a multi-source data fusion analysis model is constructed. By integrating multi-dimensional information such as crowd flow data, equipment data, energy consumption data, and transaction data in the operation of the venue, the model establishes a complete technical framework including data collection, pre-processing, fusion analysis, and decision support. A heterogeneous data fusion algorithm based on deep learning was designed to realize the spatiotemporal correlation analysis of multi-source data. A comprehensive evaluation system was constructed that included the efficiency of venue use, operating costs, service quality and other dimensions. An intelligent operational decision support model was developed. The empirical study shows that the model increases the venue utilization rate by an average of 15%, reduces operating costs by 12%, and increases user satisfaction by 18%. The research results provide a replicable technical solution and practical reference for the intelligent transformation of stadiums, which is of great significance for promoting the digital development of the sports industry.