Research on Intelligent Parsing and Key Parameter Extraction Algorithm of Collaborative Exception Log in Multi-System Environment

Multi-system environment has become an important part of modern enterprise and organization information architecture. However, the complexity of multi-system environment and the mass of log data bring great challenges to effectively analyze abnormal logs and extract key parameters. In this paper, an intelligent collaborative anomaly log parsing algorithm is proposed, which integrates several modules such as log preprocessing, feature extraction, anomaly detection and classification, collaborative parsing and post-processing. In the log preprocessing stage, multi-system logs are cleaned, formatted and normalized; In the feature extraction stage, natural language processing (NLP) and statistical techniques are used to extract key features. In the stage of anomaly detection and classification, the accurate detection and classification of anomaly logs are realized through machine learning model; In the collaborative analysis stage, the correlation between multi-system logs is combined to identify cross-system abnormal patterns; In the post-processing stage, the analysis results are verified and optimized, and the final exception log analysis report is output. This paper also designs a key parameter extraction algorithm, which combines three strategies: rule and pattern matching, statistical analysis and feature engineering, and machine learning-aided extraction. Through regular expressions, gradient lifting trees and other technical means, it extracts information that is crucial for system operation and performance monitoring from massive log data. The experimental results show that the intelligent parsing algorithm of collaborative exception log designed in this paper performs well in accuracy, recall and F1 score, especially when Word2Vec features are combined with XGBoost model, the highest performance is achieved. At the same time, collaborative analysis can identify cross-system abnormal patterns more effectively than single system log analysis, and improve the comprehensiveness and accuracy of log analysis. Through the extraction of key parameters, this paper accurately grasps the frequency of various error codes in the system, which provides strong support for subsequent troubleshooting and system optimization. The research results of this paper are of great significance for improving the operation and maintenance efficiency and ensuring the stability of the system in the multi-system environment, and provide a strong reference for further research and application.