As the exploration and development of oil and gas fields become increasingly difficult，higher requirements are put forward for the accuracy of sandstone lithology prediction. Geostatistical methods with high longitudinal resolution have been more widely used，while the insufficient reliability facing cross-well prediction grows more evident. This paper proposed a method of sandstone lithology prediction based on optimized machine learning algorithm and attribute feature. Firstly，sandstone characteristics in the seismic data and attribute volume were clarified through fine well-to-seismic calibration. Then，the optimal machine learning algorithm was selected after optimizing the attribute features and determining the sensitive logging curves. Next，the K-fold cross validation was used to obtain the optimal combination of hyperparameters.Finally，the training model with high prediction accuracy and robustness was obtained through multiple iterations. This method was applied to the sandstone lithology prediction of the 5th sand group in the Upper Guantao Formation in the eastern slope of Chengdao. Results showed that the coincidence of well points is high，and the predicted extension pattern of cross-well sandstone is consistent with the seismic data，which proves the reliability of cross-well prediction.