The Algorithmic Public Sphere: How AI is Reshaping Policymaking and Marginalizing Dissent

Introduction: The rapid development of big data has created new demands for improving the quality and accessibility of sports public services. Accurately assessing regional sports service performance is crucial for optimizing resource allocation and informing policy planning. Methodology: This study proposes a Fuzzy Integral Convolutional Neural Network (FI-CNN) that integrates deep learning with fuzzy information fusion to evaluate sports public services. The model was validated using benchmark datasets including MNIST, ImageNet, and the Stanford Sports Event Corpus. The FI-CNN’s performance was compared against a baseline Noise Fusion CNN (NF-CNN) in terms of classification accuracy and computational efficiency. Results: The FI-CNN achieved classification accuracies of 98.8%, 85.9%, and 87.1% on MNIST, ImageNet, and the Stanford Sports Event Corpus, respectively, outperforming the NFCNN and requiring less computational time. When applied to real-world data from eastern, central, western, and northeastern China, the model revealed substantial disparities in sports service provision, with eastern provinces showing the highest input, output, and overall effectiveness. Discussion: The results demonstrate that FI-CNN is highly accurate, efficient, and robust, effectively capturing complex patterns in sports service data. The observed regional disparities highlight the need for targeted policy interventions to improve service equity and overall effectiveness across different areas. Conclusion: The FI-CNN provides a reliable, data-driven decision-support tool for evaluating and enhancing sports public services. Its integration of deep learning and fuzzy information fusion offers practical significance for policymakers aiming to optimize resource allocation and improve regional sports service delivery.