Algorithm optimization for user behavior pattern recognition in intelligent slicing monitoring

Intelligent faceted monitoring has important applications in the field of data flow and user behavior analysis, which can realize real-time monitoring and optimization of system operation status. User behavior pattern recognition is the key link of intelligent faceted monitoring, which is of great significance for anomaly detection, risk warning and security policy optimization. However, existing methods still have some limitations in feature extraction, recognition accuracy and real-time performance. Aiming at these problems, this paper proposes a behavioral modeling method based on multidimensional feature extraction, optimizes the recognition algorithm by combining machine learning and knowledge-driven technology, and designs an efficient anomaly detection and warning mechanism. Experimental results show that the optimization method can improve the recognition accuracy, reduce the computational complexity, and enhance the detection ability of abnormal behavior. The research in this paper can provide a more efficient and accurate user behavior analysis solution for intelligent monitoring systems, which has wide application value in scenarios such as power data flow.