To improve the classification accuracy of text classification, Fisher information metric based on stochastic neighbor embedding ( FIMSNE) was proposed. In this paper, text word frequency vectors were taken as probabilistic density functions that were points on a statistical manifold, and their distances were defined by Fisher information metric. From the view of information geometry, t-stochastic neighbor embedding ( t-SNE ) was improved to FIMSNE. That FIMSNE outperforms t-SNE, Fisher information nonparametric embedding ( FINE) and principal components analysis ( PCA) in the whole was verified with 2D-embedding and classification task to real text dataset.%为提高文本分类的准确率,提出了费希尔信息度量随机近邻嵌入算法( Fisher information metric based on stochastic neighbor embedding, FIMSNE)。首先,把文本的词频向量看作统计流形上的概率密度样本点,利用费希尔信息度量计算样本点之间的距离；然后,从信息几何的观点出发,对 t 分布随机近邻嵌入( t-stochastic neighbor embedding, t-SNE)进行改进,实现了新算法。真实文本数据集上的二维嵌入和分类实验的结果表明：FIMSNE的性能在总体上优于t-SNE、费希尔信息非参数嵌入( Fisher information nonparametric embedding,FINE)和主成分分析( principal components analysis,PCA)。
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