Due to the combination of cloud-native architecture, distributed storage, real-time computing and multi-tenant resource scheduling, the operational attributes of big data platforms are high-dimensional, dynamic, strongly coupled and have a low labeling level. The existing operation and maintenance method is a static-threshold, manual-inspection approach that does not meet the requirements of low-latency, high-reliability and interpretable governance. This paper introduces an AI-based intelligent operation and maintenance framework for cloud-native big data platforms and focuses on the technical chain of "intelligent perception - anomaly identification - root cause localization - closed-loop handling". It unifies the modelling of multi-source data, including metrics, logs, traces, configurations and events, and integrates strong standardization, time-series predictive reconstruction, dynamic graph association, and adaptive thresholds to achieve anomaly detection. According to research, the core of intelligent operation and maintenance is not a high-accuracy single-point model, but rather the cooperation of data governance, real-time inference, alarm suppression and root cause analysis. This paper also proposes a multi-layered platform architecture and practical implementation plans to offer methodological support for transforming big data platforms from passive fault handling to proactive risk warning.

Chenghao Shi. Research on AI Driven Big Data Platform Intelligent Operation and Anomaly Detection Technology. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 2: 40-50. https://doi.org/10.38007/IJBDIT.2026.070205.

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