Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants produced in case incomplete combustion of organic materials such as coal, petroleum, wood, tobacco, and other organic polymer compounds. More than 200 PAHs have been discovered to date, and many of them have carcinogenicity. PAHs are widely distributed in the environmentthat we live in. PAHs in water are mainly derived from domestic sewage, industrial drainage and atmospheric deposition. In this paper, three-dimensional fluorescence spectroscopy combined with BP (back propagation) neural network and alternating trilinear decomposition (ATLD) algorithm for qualitative and quantitative analysis of PAHs in water. In this paper, two PAHs, ANA and FLU, were used as analytes, and samples were prepared using methanol (spectral level). The samples were detected using a FS920 steady-state fluorescence spectrometer. The excitation wavelength was set at 200～370 nm, and data were recorded at intervals of 10 nm. The emission wavelength was 240~390 nm, and data were recorded at intervals of 2 nm. Setting the initial emission wavelength always lags the excitation wavelength by 40 nm to eliminate the interference of the first-order Rayleigh scattering. The sample data are then preprocessed using the BP neural network method. The BP neural network is used to compress the measured three-dimensional fluorescence data based on the principle of Error Back Propagation Training (BP). The method has flexible network structure and strong nonlinear mapping ability. The number of neurons in the input layer, the hidden layer, and the output layer can be set according to actual conditions, and the performance is also different when the structure of the network is different. Subsequently, the pre-processed three-dimensional fluorescence spectrum data were decomposed using the ATLD algorithm. Before the decomposition, the nuclear consistent diagnosis method is used to determine the number of components of the sample to be tested is 2. The results show that the excitation and emission spectra of ANA and FLU are very similar to the target spectrum, which can realize the rapid qualitative and quantitative analysis of PAHs (ANA and FLU) with severe spectral overlap. “Mathematical separation” replaces “chemical separation”. The predicted samples are imported into the trained BP neural network, and the network mean square error (MSE) of the sample data to be tested is less than 0.003, and the peak signal-to-noise ratio (PSNR) of the network is greater than 120 dB (typical peak signal in data compression). The noise ratio is between 30 and 40 dB, the higher the better. It can be seen that the BP neural network has better compression effect on the sample data. After BP neural network training, the fitting degree between the output value and the target value is high, and the fitting coefficient is 0.998, which has better data compression effect. Using the ATLD algorithm to decompose the samples to be tested, the average recoveries were 97.1% and 98.9%, and the predicted root mean square errors were 0.081 8 and 0.098 5 μg·L-1. Three-dimensional fluorescence spectroscopy combined with BP neural network and ATLD can achieve a rapid detection of trace amounts of PAHs.