Background: To explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of tongue squamous cell carcinoma (TSCC).Methods: Retrospective analysis of 87 TSCC patients who were randomly divided into a primary cohort and a test cohort. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI) and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to the set and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results: In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74.Conclusion: In this model, there was a significant relationship between radiomics characteristics and the degree of pathological differentiation, and the degree can be predicted from MRI features using machine learning. Advances in knowledge: Texture analysis and prediction of the differentiation degree of TSCC by MRI are not only a breakthrough and innovation in the diagnosis of TSCC but are also of significance in clinical diagnosis and treatment.