With the rapid development of the Internet of Things (IoT), there are more and more big data generated in the IoT system, which requires effective processing and analysis. Traditional data processing methods cannot meet the processing needs of big data in the IoT system, so it is necessary to study new big data processing technologies in the IoT system. This paper has proposed a big data algorithm, which uses data mining technology in big data to process sensor and device data. In the data pre-processing stage, the algorithm and device use data cleansing and other technologies to ensure data quality and reliability. In the feature extraction and selection stage, the algorithm and device adopt adaptive feature extraction and selection techniques to extract key features of the data and reduce the dimensionality and complexity of the data. In the experiment, this article tested and evaluated the algorithm to verify its performance. The experimental results showed that the F1 value of the model established in this study was 0.87, and the training time was the shortest, only 9 seconds. This algorithm and device can effectively improve the efficiency of data processing and analysis, as well as improve the accuracy and reliability of data processing. Compared with traditional data processing methods, this algorithm and device have better performance and application prospects. The algorithm and device also have good robustness and scalability, and can adapt to different data processing and analysis needs. The algorithm based on big data mining technology is an effective big data processing technology of the IoT system, which can improve the efficiency of data processing and analysis, and improve the accuracy and reliability of data processing.

Guangjian Ma. Artificial Intelligence Algorithm and Device for Big Data Processing of the IoT System. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 8: 82-88. https://doi.org/10.25236/AJCIS.2023.060810.

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