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With the rapid development of information technology, the indicative method on the information characteristics keep expanding,high-dimensional feature emerge and grow with a massive trend. These high-dimensional feature contain much redundant and irrelevant feature, which will result in the curse of dimensionality. This situation will further lead to higher requirements and more challenges for the classification and recognition algorithm,need the feature selection algorithm to reduce the dimension of eigenvector and data noise.Aim at the dimension disaster introduced by the high dimension eigenvector,and the application oriented ATR algorithm,porpose an improved algorithm based on the sequential floating forward selection,by optimizing the repeat number of cross-test.The results of the simulation experiments shows that on the premise of the high classification accuracy,this improved algorithm can upgrade the calculation speed effectively and could maintain a more astringent and more stable confidence interval what means a better accuracy.