针对新一代燃气轮机燃烧过程中存在的热声振荡问题，通过构建复杂网络模型，对高频数据信号的多种时域分析方法进行了复验和对比统计分析。结果表明，在复杂网络模型中，节点强度和网络直径这两个特征表征参数比传统的时域分析方法（即均方根和时间峭度）能更早的对热声振荡发出预警。耦合预测效果和数据处理时间，优选节点强度来构建复杂网络模型。最终将该方法应用于多喷嘴微混燃烧器燃烧实验数据分析中，发现该方法的特征转折点比极限环分析和统计分析方法的特征点领先约2.3 s。

Aiming at the thermoacoustic oscillation in the combustion process of a new generation of gas turbine, several time-domain analysis methods of high frequency data signals were retested and compared by constructing a complex network model. The results show that the two complex network models, node strength and network diameter, can give an earlier warning of thermoacoustic oscillations than the traditional time-domain analysis methods (root mean square and time kurtosis). Coupling prediction effect and data processing time, the node strength is preferred to construct the complex network model. Finally, the method was applied to the experimental data analysis of multi-nozzle micro-mixing burners, and it was found that the characteristic turning point of the method is about 2.3 s ahead of limit cycle analysis and the characteristic point of statistical analysis.