Based on the laser-induced breakdown spectroscopy assisted with laser-induced fluorescence (LIBS-LIF) technology, this paper proposes a feature equilibrium method to fuse the feature data of homologous laser-induced breakdown spectroscopy (LIBS) to analyze the lead content in kelp with high precision. First, LIBS-LIF technology is employed for univariate quantitative analysis of lead in kelp, with a detection limit of 0.085 mg/kg, which is well below the limit set by national standard, and the sensitivity meets the detection requirements. Then, the LIBS-LIF spectra are interpolated using cubic spline interpolation, while the peak fragment selection for the LIBS spectra is performed using a genetic algorithm. This process resulted in a feature-equilibrium spectrum with matching feature dimensions. Finally, the proposed method is validated using three machine learning algorithms: ridge regression (RR), random forest regression (RFR), and support vector regression (SVR). Results show that the feature equilibrium method significantly improved the prediction accuracy and generalization ability of the model, among which the SVR model achieves the best quantitative performance, with the coefficient of determination, root mean square error, and average relative error for prediction set are 0.957, 0.251 mg/kg, and 7.36%, respectively. It is proved that the proposed method offers high quantitative accuracy, which provides a new approach for achieving high-sensitivity and high-precision detection of lead in kelp.