Studies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. In this paper, the measurement data of the micro air quality detector is calibrated with the help of the LASSO regression and NARX neural network combination (LASSO-NARX) model using the data measured by the national control point. First, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant. Second, LASSO regression is used to give the quantitative relationship between pollutant concentration and various influencing factors. Third, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Finally, several indicators such as Root Mean Square Error, goodness of fit, Mean Absolute Error and Relative Mean Absolute Percent Error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. The LASSO-NARX model performed equally well on the training set and test set, indicating that the model has excellent generalization capabilities. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3% to 91.7%.