In order to reduce the sulfur and olefin content and the loss of octane number, to ensure the clean production of gasoline. Based on the data accumulated by the operation of the S Zorb device, first use Lasso to initially screen the modeling variables, and calculate the index factor contribution based on the BP neural network, and further screen out 15 main variables to build the octane loss prediction model. Secondly, the four models are compared and analyzed, and it is concluded that the BP neural network has better prediction accuracy and is more suitable as an octane loss prediction model. After ten-fold cross-validation, the average MSE value is 0.027193 and the average R2 value is 0.90487, which verifies the reliability of the model. sex. Finally, under the premise that the sulfur content of the oil is not greater than 5μg/g, the main variables are optimized and controlled in combination with multiple linear regression. The results show that it is necessary to change multiple variables at the same time to reduce the octane loss by more than 30%. The multiple linear regression model has good prediction accuracy and can regulate the main variables in a certain proportion. The trajectory of the alkane number and sulfur content.