For the reason of the traditional sparse representation classifier is not sensitive to the changes of characteristics only has enough training samples, a face recognition method based on Shearlet multi-orientation adaptive weighted fusion and sparse representation is proposed. In order to extract the multi-orientation information and reduce the dimension of the features, images are decomposed in multi-scale and multi-direction by using Shearlet, and the subband coefficient matrices are obtained. Then, the directional sub charts on the same scale are sorted in the main direction according to the sizes of the variances of the subband coefficient matrices. Furthermore, using the energy and the mean values of subband coefficient matrices, the face sub charts are weighted fused. Finally, Shearlet multi-orientation feature fusion is applied to construct sparse representation classifiers for sparse representation of coefficient vectors. The experiments taken under the ORL, FERET, and YALE database are used to verify the effectiveness of the proposed method, and the results show that the proposed method can effectively enhance the robustness of the external environment changes and improve the recognition rate.