碳纤维复合材料在拉伸损伤试验中，会产生大量的声发射信号。对声发射信号的数据进行了分析，找出了碳纤维复合材料的损伤演变规律。对数据进行聚类分析，将数据分成由类似对象组成的多个簇，找出簇与损伤之间的对应关系。通过对聚类后数据进行建模，得到碳纤维复合材料拉伸损伤识别模型。由于声发射信号的特征是一个多维向量，特征之间存在一定的关联，为了提高建模速度，需要对数据进行降维，以选择主要影响因素的特征。为此，采用遗传算法对数据进行降维，去掉冗余的特征，而保留主要的特征。最后将处理前后数据分别代入到BP神经网络，对其进行损伤识别。试验结果表明：采用遗传算法优化对数据进行降维，其建模时间更短，识别效率更优。

Carbon fiber composites produce a large number of acoustic emission signals in tensile damage experiments. The data of acoustic emission signals are analyzed to find out the damage evolution law of carbon fiber composites. The data is clustered and analyzed, and is further divided into multiple clusters composed of similar objects to find the correspondence between the cluster and the damage. The model of tensile damage identification of carbon fiber composites was obtained by modeling the data after clustering. Since the feature of the acoustic emission signal is a multi-dimensional vector, there is a certain relationship between the features. In order to improve the modeling speed, the data needs to be dimension-reduced to select the characteristics of the main influencing factors. To this end, the genetic algorithm is used to reduce the dimensionality of the data, and the redundant features are removed, and the main features are retained. Finally, the data before and after processing are brought into the BP neural network to identify the damage. The experimental results show that more optimized results can be achieved by genetic algorithm for dimensionality reduction with shorter modeling time and better recognition efficiency.