The safe production of coal mines has always been one of human's key research subjects. In numerous safety accidents in coal mining, gas accidents account for most of them. Real-time and accurate prediction of gas concentration in underground production lines and anticipating whether the production environment is in a safe state is critical for the safety of coal mines.  Aiming at this problem, we propose a gas concentration prediction method based on the random hidden layer neural network trained by NSGA-II (BNSGA-II NN). On the one hand, fewer parameters need to be set in the NSGA-II, and they are convenient to use. On the other hand, the cross variation mechanism in the NSGA-II avoids the problem of falling into local optimal solution in the traditional methods. To demonstrate the prediction quality of the trained neural network with random hidden weight using the NSGA-II, we compare the BNSGAII NN with PSOGSA NN through experiments. Experimental results show that the prediction effect of the BNSGA-II NN is significantly better than that of the PSOGSA NN.