With the rapid development of spectroscopy and fluorescence detection technology, monochrome fluorescence labeling is unable to analyze cell samples accurately and has been gradually replaced by two-color or multi-color fluorescence labeling. In the multicolor fluorescence analysis, since the cells were labeled with a variety of fluorescein usually, partial spectral overlap will occur in the emission spectrum, which need to be decomposed into an independent spectral peak to analyze accurately. Aiming at it, optimized BP neural network based on genetic algorithm (GA_BP) were used for overlapping spectral peak analysis. Firstly, the concrete structure of BP neural network was determined, and the overlapping peak was pre-processed by quadratic differential to find out the number and positions of single peaks as the characteristic value of overlapping peaks to be the input layer of BP neural network; in addition, weights and thresholds of BP neural network were Initialized, and optimal parameters like initial population and population size of the genetic algorithm were selected by using the advantage of global search; after a series of genetic evolution operations like selecting, crossing and mutating, the individuals containing the optimal weights and thresholds of BP neural network were obtained; and then the optimal parameters of the network were selected to carry out network training, which the width and intensity of the independent peak can be calculated from the output node of the optimized BP neural network; finally, combined with the eigenvalues of overlapping peak identified by quadratic differential, independent spectral peak can be separated. The randomly generated Gaussian overlapping peaks model was used as experimental simulation data, and the decomposition experiments showed high precision of the peak intensity and peak width. Wherein, the maximum relative error of decomposition of two overlapping peaks was 0.30% and 3.57%, and which of the three overlapping peaks was 0.64% and 3.83%. It can also be decomposed when the four overlapping peaks. Moreover, compared the GA_BP network model with the unoptimized BP neural network model, the results showed that the GA_BP network could reach the preset error value after five steps, while the unoptimized network model takes 19 steps. This further proves that the GA_BP network model converged faster with a fairly high precision that can be widely used for the decomposition of spectral and other overlapping peaks, which has a certain practical value compared with traditional methods.