Based on the laser induced breakdown spectroscopy technique,the slag spectrum is obtained by breaking the plasma in the slag,combining the genetic algorithm with the BP neural network,and optimizing the weight and threshold of the neural network by genetic algorithm.The neural network model quantitatively detects the content of Ca in the slag elemental spectrogram.The mass fractions of the five Ca elements #1,#2,#3,#4,#5 are 29.4 %,40.37 %,and 37.13 %.43.88 %,38.68 %,and the relative error of the calculated test samples are 4.7 %,5.2 %,5.8 %,4.1 %,3.3 %,and the relative error is below 6 %.The detection accuracy is significantly better than the BP-ANN method and spectral analysis.The commonly used free calibration method shows that the quantitative analysis of slag based on genetic neural network has better detection effect.