中圖分類號：TP311.5 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.246037
中文引用格式： 李雄清，楊彭程，宋欣卉，等. 基于LSTM模型的智能服務治理方案研究與實踐[J]. 電子技術(shù)應用，2025，51(9)：73-78.
英文引用格式： Li Xiongqing，Yang Pengcheng，Song Xinhui，et al. Research and practice of intelligent service governance scheme based on LSTM model[J]. Application of Electronic Technique，2025，51(9)：73-78.

Research and practice of intelligent service governance scheme based on LSTM model

Abstract： In order to solve the problem that the airline's website is easy to cause system anomalies and difficult to manage quickly when the number of visitors fluctuates greatly, this paper proposes an intelligent service governance Scheme based on Long Short Term Memory (LSTM) model， which is dased on SpringCloud microservice governance framework, incorporates LSTM model to analyze and predict the system metrics, and combines the Cloud-Native architecture. Using LSTM to train and output the prediction model for historical index data, it realizes the trend prediction of indexes; combining with Cloud-Native infrastructure, it generates active horizontal container group auto scaler based on the prediction results, realizing the pre-scaling of basic resources; according to the index prediction results and the change trend, it automatically sends out and recycles the management policy, realizing intelligent traffic management. Practice has shown that this scheme can effectively mitigate system risks by preparing for resource scaling and service management in advance based on model prediction results.

Key words : intelligence；service governance；elastic scaling；fusion limiting；trend prediction