The invention discloses a face recognition method based on extraction of multiple evolution features. The method comprises the steps as follows: (1), classification of initial samples: the initial samples are divided into three parts, including training samples for feature extraction, training samples for weight evolution and test samples respectively; (2), feature extraction of the training samples: the training samples are subjected to feature extraction with a multiple seed space method, such as PCA (principal component analysis), LDA (linear discriminant analysis), LPP (locality preserving projection) or the like; and (3), multiple feature fusion evolution: features obtained with different feature extraction methods are fused according to a form that Phi is equal to the sum of Omega 1 Phi 1, Omega 2 Phi 2, ..., and Omega n Phi n, and the like, wherein Omega is a weight coefficient. An optimal weight coefficient is obtained with a genetic algorithm, so that fused features have better recognition effects than prior features. The face recognition method has the advantages that the principle is simple, the method is unique, the application is easy, and the like.