传统主特征向量动力学分析（leading eigenvector dynamics analysis, LEiDA）在数据降维策略采用线性降维方法，忽略了大脑的非线性信息，同时在大脑状态识别使用k-means硬聚类方法，粗略的标准划分容易忽略一些非典型子状态及特征。针对这一问题，提出基于相位相干图自编码器（phase coherence graph autoencoder, PCGAE）分析方法。该方法利用PCGAE对大脑数据降维，并使用软硬聚类联合分析方法进行特征提取并对差异结果进行特征选择，最后利用支持向量机构建分类模型来检验分类效能。结果表明：所提方法在降维上具有更加优秀的聚类效应，并且在分类结果优于传统的LEiDA分类结果，达到83.7%。证明了该方法可以更好地进行动态功能模式分析和疾病分类。

The traditional leading eigenvector dynamics analysis(LEiDA) employs linear dimensionality reduction methods, which overlook the nonlinear information of the brain. Additionally, the use of k-means hard clustering for brain state identification results in rough classifications, potentially neglecting atypical sub-states and features. To address this issue, the phase coherence graph autoencoder(PCGAE) analysis method was proposed. This method utilized the phase coherence graph autoencoder for dimensionality reduction, followed by the combined soft-hard clustering analysis method for feature extraction and differential feature selection. Finally, the support vector machine was used to build a classification model to evaluate classification performance. The results show that the proposed method achieves superior clustering effects in dimensionality reduction and outperforms traditional LEiDA classification results, with an accuracy of 83.7%. These findings suggest that the proposed method provides a better approach for dynamic functional pattern analysis and disease classification.