【Abstract】 Objective To explore the value of machine learning models based on T2WI and ADC imaging radiomics features in differentiating early cervical cancer from chronic cervicitis. Methods A retrospective analysis was conducted on 80 patients with pathologically confirmed cervical lesions in Shaoguan Maternal and Child Health Hospital from September 2019 to February 2023, including 34 with early cervical cancer(positive group) and 46 with chronic cervicitis(negative group). The patients were split into a training set (56 cases: 24 positive and 32 negative), and an independent test set (24 cases: 10 positive and 14 negative). T2WI and ADC images of each patient were obtained, and a total of 837 radiomics features were extracted from cervical VOIs by 3D slicer software and the PyRadiomics software module. Three data-normalization methods, two dimensionality-reduction methods , four feature selection methods and ten machine learning methods were used. The optimal number of features in the modeling process was set to 1 to 12. Ten-fold cross-validation was performed with the training data set to determine the model hyperparameters. Model performance was evaluated by AUC with the independent test set. Results A total of 2880 machine learning models were established; of these, Mean_PCC_RFE_5_SVM had the best performance. Conclusions Machine learning models based on T2WI and ADC imaging radiomics features have significant value in differentiating early cervical cancer from chronic cervicitis. Compared with other machine learning models, the support vector machine models had higher diagnostic efficiency.