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In order to solve the problem of insufficient information extraction of training samples and category labels based on sparse representation classification method, especially when the training samples and samples to be tested are polluted by noise, and the complexity of the algorithm is high, a face recognition algorithm based on Gabor feature and weighted collaboration is proposed. At first, it is necessary to complete the extraction of Gabor features of each scale and direction contained in the face image. Gabor features are introduced into the sparse representation. The Gabor feature matrix after dimension reduction is taken as a super-complete dictionary, and then the sparse representation is enhanced by weighted cooperative representation to obtain the sparse representation coefficient under the dictionary. Then, the test samples were classified and recognized by using the enhancement coefficient and the label matrix of the training samples, and the Gabor feature and weighted cooperative representation classification method were obtained. Experiments on Yale face database, Extended Yale B and AR face database and FERET face database show that the new algorithm has better recognition rate and shorter computation time.