In the process of multi-modality magnetic resonance image （MRI） data acquisition， there will be different degrees of modality data missing. However， most of the existing completion methods only aim at random missing， which cannot recover strip and block missing. Therefore， this paper proposes a classification framework of smooth tensor completion algorithm based on multi-directional delay embedding. Firstly， the folded tensor is obtained by multi-directional delay embedding of missing data. Then， the completed tensor is obtained by smoothing tensor CP decomposition. Finally， the reverse operation of multi-directional delay embedding is used to obtain the completed data. The algorithm is used to classify high-level and low-level tumors on the BraTS glioma image data set and compared with seven baseline models. The average classification accuracy of the proposed method achieves 91.31%， and experimental results show that the method has better accuracy compared with the traditional complement algorithm.