The variation trend in pollutant concentration is of great significance for environmental monitoring. At present, the output of various feedforward neural network prediction models is only related to the current input, so it is impossible to study the dependence of pollutant data before and after. Multiple pollutants have the same emission source, and there is a potential correlation between pollutants. The change in one pollutant may reflect that in another pollutant, so the influence of other sensitive parameters should be considered in the prediction. To solve the above two problems, this study proposes a regional key pollutant concentration prediction method based on sensitive parameter discovery. The association rule algorithm and multiple regression analysis are used to mine the sensitive parameters of each pollutant, and a multivariable LSTM prediction model is constructed. The pollutants to be predicted and their sensitive parameters are taken as the characteristic variables of the model to predict the pollutant concentration. The experimental results show that the proposed method can reliably predict the variation trend in pollutant concentration and it performs better than the LSTM model without relation discovery.