Optimization of Artificial Intelligence Algorithm Driven by Big Data

This article aims to explore the optimization method of artificial intelligence (AI) algorithm driven by big data. Firstly, this article analyzes the characteristics of big data and its important role in the optimization of AI algorithm, and clarifies the research background and significance. This article takes e-business data mining as an example, and studies the prediction of consumers' consumption tendency through data preparation, preprocessing, algorithm selection and optimization. In the study, the performance of the algorithm before and after optimization is compared, and the influence of different features on the prediction results is analyzed to verify the effectiveness of AI algorithm optimization driven by big data. The results show that the optimized algorithm has significantly improved the prediction accuracy and recall, which is 20.16 % higher than that before optimization. At the same time, F1 score and AUC-ROC value have also been significantly improved, which proves the success of the optimization strategy. To sum up, the research in this article shows that the optimization of AI algorithm driven by big data can significantly improve the performance of the algorithm and provide strong support for practical applications such as ebusiness data mining.