The manual labeling of abundant data is laborious and the amount of Magnetic Resonance Imaging （MRI） data from a single imaging center is limited. Concerning the above problems， a Magnetic Resonance Semi-Supervised Learning （MRSSL） method utilizing multi-imaging center labeled and unlabeled MRI data was proposed and applied to knee abnormality classification. Firstly， data augmentation was used to provide the inductive bias required by the model . Next， the classification loss and the consistency loss were combined to constraint an artificial neural network to extract the discriminative features from the data. Then， the features were used for the MRI knee abnormality classification. Additionally， the corresponding Magnetic Resonance Supervised Learning （MRSL） method only using labeled samples was proposed and compared with MRSSL for the same labeled samples. The results demonstrate that MRSSL surpasses MRSL in both model classification performance and model generalization ability. Finally， MRSSL was compared with other semi-supervised learning methods. The results indicate that data augmentation plays an important role on performance improvement， and with stronger inclusiveness for MRI data， MRSSL outperforms others on the knee abnormality classification.