Abstract: 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.