The Analysis of Communication Strategy of Disabled Sports Information Based on Deep Learning and the Internet of Things

The ever-growing landscape of Internet of Things (IoT) technology and the evolution of deep learning algorithms have ushered in transformative changes in the communication strategy for disseminating information on disabled sports. This specialized information resource aims to provide relevant support and services related to sports activities for disabled individuals. This study investigates the communication strategy of disabled sports information driven by deep learning within the framework of the IoT and assesses the practical application performance of the proposed model. To achieve this objective, an appropriate deep learning model for the dissemination of sports information for the disabled is selected through a thorough literature review. Subsequently, an experimental framework is proposed for comprehensive performance verification, evaluating the model's performance in reasoning time and user satisfaction through comparative experiments. By constructing deep learning models, extensive data on disabled sports activities are analyzed, enabling the identification and prediction of key factors in information dissemination. The results indicate that the proposed sports information dissemination model outperforms similar models across various performance metrics, particularly in real-time performance and user experience. Comparative analysis with attention-based deep neural networks and traditional machine learning algorithms reveals that the proposed model achieves an accuracy rate as high as 0.85, significantly surpassing the 0.78 and 0.82 accuracies of these models, respectively. Moreover, the proposed model demonstrates the shortest inference time (15ms), surpassing both aforementioned models. This study validates the relative advantages of the proposed model through comparison with similar studies, offering a novel solution for the dissemination of sports information for the disabled.