In order to rapidly detect the heavy metal pollution in shellfish, the contents of heavy metal copper in tegillarca granosa were predicted by Laser-induced Breakdown Spectroscopy (LIBS) combined with the consensus strategy of mulit-models. By analysis of LIBS copper characteristic spectral lines, the characteristics of copper peak intensity, area, peak ratio of copper′s peaks, were extracted, and multivariate linear regression models were developed respectively. Besides, partial least regression model was calibrated based on copper characteristic spectra region. Among the residual vectors of these calibration member models, the Lagrange multiplier method was used to optimize the linear combination between these member models, aiming at reducing the correlation between member models and minimizing the mean squared error of the consensus model. Through external validation from the prediction set, the consensus model was performed better than that of any member models, with the root mean square prediction error of 20.641 mg/kg, as well as the correlation coefficient of 0.835, and the prediction deviation was only -0.473. Results show that the LIBS technology combined with the consensus model can be used for the quantitative detection of heavy metals of aquatic products.