With the explosive growth in data scale and complexity in the era of big data, traditional data processing methods face severe challenges in efficiency, accuracy, and adaptability. This paper focuses on the optimization mechanisms of artificial intelligence (AI) algorithms in data processing, aiming to construct a multiobjective optimization framework through algorithmic structural innovation and computational resource coordination. The study systematically reviews the applicability boundaries of algorithms such as machine learning and deep learning, proposes a dynamic pruning distillation joint optimization model, and designs an adaptive scheduling strategy for heterogeneous computing resources. Experiments show that the optimized algorithm achieves a 12.7% improvement in accuracy on benchmark datasets while reducing inference latency by 41.3%. This research provides theoretical support and technical pathways for efficient data governance in scenarios such as industrial IoT and smart healthcare.
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Share and Cite
Xu, X. (2025) Research on Optimization of Artificial Intelligence Algorithms in Data Processing. Global Education Bulletin, 2(6), 110-118.