Objective: To investigate the ability of ultrasound radiomics in the differential diagnosis of pleomorphic adenoma and adenolymphoma lymphoma in parotid gland. Methods: The ultrasonic images and clinical data of 133 cases of pleomorphic adenoma and 99 cases of adenomas confirmed by postoperative pathology were retrospectively collected from the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital) from January 2016 to September 2021. According to the ratio of 7∶3, there were 162 cases in training set and 70 cases in verification set.The Region Of Interest (ROI) of the tumor was manually delineated on ITK-SNAP software, and the quantitative features of two-dimensional ultrasonic parotid focus imaging were extracted by Pyradiomics software. The extracted features were regularized and Spearman correlation analysis was performed. Then, the model of Least Absolute Shrinkage and Selection Operator (LASSO) can be used to screen out the best features. Three machine learning algorithms, namely support vector machine (SVM), K-Nearest Neighbor (KNN) and Decision tree, were used to construct the characteristics of ultrasonic image omics model, and establish the differential diagnosis ability of pleomorphic adenoma and adenoma. Receiver Operating Characteristic (ROC) curves were plotted to evaluate the diagnostic efficiency of each model. Delong test was used to evaluate the differences of the models, and decision curve analysis (DCA) was used to evaluate the clinical application value of the models. Results: When ultrasound radiomics was used to identify common benign tumors in parotids, 21 radiomics features were extracted. The AUC, sensitivity, specificity and accuracy of the model constructed by SVM, KNN and Decision Tree algorithm were 0.848, 0.842, 0.814 and 0.826, respectively. 0.721, 0.947, 0.481, 0.652; 0.620, 0.684, 1.000, 0.608. Through DeLong test, it is found that SVM algorithm is superior to the model constructed by other two algorithms. Conclusion: Ultrasound radiomics can be used to distinguish pleomorphic adenoma from adenolymphoma by machine algorithm.