Using python language, the fuel consumption prediction model is built based on the vehicle operation data collected by OBD.Taking vehicle running state parameters such as vehicle speed v, engine speed n, intake pipe absolute pressure P, throttle position TP, coolant temperature CT, load rate L, idle time IT, acceleration an as independent variables and 100 km fuel consumption as dependent variables, the correlation intensity between parameters and dependent variables is sorted by SelectKbest function and briefly analyzed.The (MLP) neural network model of multilayer perceptron based on tensorflow and the multiple linear regression model of support vector machine (SVM) are used to predict the fuel consumption at the same time.Support vector machine model RMSE is 0.088 MAE is 0.56 tensorflow neural network model RMSE is 0.132 MAE is 0.70.The results show that the two models are accurate in the prediction of fuel consumption, which can provide a theoretical basis for further elucidating the relationship between vehicle fuel consumption and vehicle running state parameters.