This paper uses machine learning and multivariate linear regression model to study air quality index of Xian City in nearly one year, firstly uses random forest philosophy to complete the data, then uses crossvalidation on the neural network model to select the optimal number of hidden layer nodes and iterations, and finally by comparing the fitting effect of the two models, finds that neural network model is significantly better than multivariate linear regression model for the prediction effect of air quality index.