Computer-Aided Prediction and Structural Optimization of the Sports Industry using Machine Learning

With the vigorous rise of the global sports industry, accurately estimating the industrial scale and implementing effective structural optimization strategies have become the core elements to enhance the competitiveness of this field. This study introduces a new machine learning-based approach aiming to accurately predict the scale of the sports industry and optimize its structural layout. A comprehensive data analysis framework was constructed by comprehensively considering a number of internal and external driving factors including policy support, macroeconomic conditions, population dynamic changes, etc. By using a variety of cutting-edge machine learning technologies, the data set is deeply trained and verified, in order to screen out the optimal model to predict the future development trend. On this basis, a set of detailed optimization schemes of the sports industry structure is formulated, aiming at promoting the efficient allocation of resources and strengthening the synergy of all links in the industrial chain. The experimental data reveal that when using the association vector machine model to predict sports industries of different scales, the prediction can be completed within 4.3 seconds at the fastest, and the integration degree of 4.8 (within the range of integration degree of 0 to 5) can be achieved, which is helpful to promote the maximization of cross-regional industrial value. Taking Jiangsu as an example, the industrial scale predicted by this model can reach 603.521 billion yuan, and based on this, structural optimization suggestions are put forward, which is expected to drive economic growth of about 35 million yuan. Experiments show that the prediction model not only has high accuracy, but also its proposed structural optimization strategy has great practical guiding significance, which provides solid decision support for policymakers.