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The current practice of recognition spectra manually is no longer applicable to a large extent. This work is particularly focused on helping astronomers finding their interesting celestial objects. In this paper an efficient hierarchical clustering data mining method based on principal component analysis (PCA) is proposed. Massive stellar spectral data are clustered by improved hierarchical clustering method after dimensionality reduction by PCA.The singular points are found out after definition according to experience. An application implemented in the automated spectral analysis system based on the method is carried out and some significative data are found out.