Objective To investigate the feasibility of texture analysis based on laparoscopic ultrasound images for distinguishing clear cell renal carcinoma (ccRCC) from non-clear cell renal carcinoma (non-ccRCC). Methods From December 2012 to June 2020, the laparoscopic ultrasonography data of patients with renal cell carcinoma confirmed by pathology in Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology were analyzed retrospectively. The region of interest (ROI) was drawn manually by ITK-SNAPE software on the laparoscopic B-mode ultrasound images, and the python-based Pyradiomics package was used to extracted the texture features. Intra-group and inter-group correlation coefficients were used to select features with good stability and reproducibility. The maximum correlation minimum redundancy (mRMR) and minimum absolute contraction and selection operator (LASSO) algorithms were used to select the features and develop the model. The diagnostic performance of the model was evaluated by ROC curves. Results A total of 83 RCC lesions were included There were 66 cases of ccRCC and 17 cases of non-ccRCC according to the pathological results. The area under the curve of the predictive model based on the six texture features was 0.860, with sensitivity 0.765, specificity 0.864 and accuracy 0.843 for distinguishing ccRCC from non-ccRCC Conclusion The texture analysis based on laparoscopic ultrasound images can be used for distinguishing ccRCC from non-ccRCC.