Noise monitoring systems can automatically measure decibel level and process various sound environment information in real time. However, in their practical application, the noise decibel is affected by many factors such as temperature, humidity and atmospheric pressure, which leads to the errors between measured and actual values. In view of this, the correction based on relevant technologies becomes a necessity for the accuracy improvement of noise measurement. This study adopts linear regression and Back Propagation (BP) neural network to investigate the factors and coefficients of the prediction model and analyzes the correlation of factors in the model. As a result, the automatic correction model of noise monitoring is obtained. The test effect of automatic data correction by linear regression and BP neural network indicates that the fault tolerance of measurement data is optimized and the accuracy of data correction is improved. Further, the determination coefficient (R²) of the prediction model is greatly increased.