Objective To investigate the value of texture analysis based on laparoscopic ultrasound images of small renal masses in discrimination of clear cell renal cell carcinoma and angiomyolipoma. Methods 66 patients with clear cell renal cell carcinoma and 58 patients with angiomyolipoma confirmed by pathology in Tongji Hospital of Huazhong University of Science and Technology from December 2012 to June 2020 were analyzed retrospectively. The patients were randomly divided into training group (n=87) and verification group (n=37). ROI was drawn manually by ITK‐SNAP software. The texture features were extracted by python‐based pyradiomics package. Intra- and interclass correlation coefficients were used to select the features with excellent stability and repeatability. The minimum-redundancy maximum-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection and model development for discriminating clear cell renal cell carcinoma from angiomyolipoma. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the model. Result 124 patients with small renal masses confirmed by pathology after resection were analyzed retrospectively, including 66patients with clear cell renal cell carcinoma and 58 patients with angiomyolipoma. The area under the receiver-operating( AUC), sensitive and specific were 0.911、0.891、0.854in the training group and 0.900、0.950、0.647in the validation group. Conclusion Texture analysis of laparoscopic ultrasound images of small renal masses could be used to discriminate clear cell renal cell carcinoma from angiomyolipoma.