To address the problem of low recognition rate of QR (Quick Response) codes under changes in illumination, pollution, and damage, a QR-code recognition algorithm based on multiblock local binary patterns (MB-LBP) combined with an improved grey wolf optimization (GWO) algorithm for optimizing a support vector machine (SVM) is proposed. Firstly, the lifting wavelet transform is used to separate the high- and low-frequency components of the image, while the second-level low-frequency and horizontal high-frequency components are divided into nonoverlapping sub-blocks. The MB-LBP features of each sub-block are separately extracted and fused. Then, principal component analysis is applied to reducing the dimension of the sample set. Finally, the classification model of the QR-code data is established using the SVM algorithm. To further improve the classification accuracy, the nonlinear convergence factor based on a logarithmic function is introduced to improve the optimization performance based on the standard GWO; the improved GWO is used to optimize the SVM model. The recognition performance is tested according to different combination modes of high and low frequencies and the SVM optimization algorithm. The experimental results show that the proposed algorithm significantly improves the recognition rate and classification accuracy, and it is highly robust.