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Identification methods for sparse representation requires a lot of training samples and high over-complete dictionary feature redundancy problem, a combination of over-complete dictionary learning and PCA dimension small sample speech emotion recognition algorithms. Firstly, the PCA dimension reduction methods feature reduction, feature and then treatment for the over-complete dictionary training and recognition sparse representation, which gives a speech emotion feature sparse representation, and to determine the specific steps of the new algorithm. To verify its validity, in Under the same number of features, the method and BP, SVM compare and contrast, analyze the impact before and after the speech emotion feature sparse speech emotion recognition rate, time-efficient and space-efficient. experimental results show that the recognition rate of the proposed method than High SVM and BP compared to pre-thinning characteristics using eigenvectors easier after thinning processing, the av