As an important fossil energy source, oil is an indispensable part of human society’s production activities. When the oil is mined and used, it could be leaked inevitably. The leaked oil will pollute the ecological environment. Therefore, it is necessary to deal with oil spills in a timely manner. Accurate identification of petroleum species is a prerequisite for handling oil spills. Petroleum contains a variety of substances with fluorescent properties. Therefore, fluorescence spectroscopy is an effective method for detecting petroleum. Due to a large number of components in the oil, the spectral information overlaps seriously, and the identification is difficult. The third-order calibration method has the “third-order advantage”. It can distinguish the data under high collinearity and high noise level. Alternating weighted residue constraint quadrilinear decomposition (AWRCQLD) algorithm is a third-order correction method. AWRCQLD algorithm has the advantages of faster convergence speed and insensitivity to component numbers. Therefore, in this paper, the three-dimensional (3D) fluorescence spectroscopy combined with AWRCQLD algorithm is used to detect the mixed oil. First, sodium dodecyl sulfate (SDS) was prepared as a solvent under three salinity conditions. Under each salinity condition, jet fuel and lube were mixed according to different concentration ratios. Thus, 24 calibration samples and 9 prediction samples are obtained. Secondly, using FLS920 fluorescence spectrometer to acquire spectral data of the experimental samples. Then, the effect of scattering was removed by using blank subtraction, and the number of components in the mixed oil is estimated by the core consistent diagnosis method. Finally, using the AWRCQLD algorithm to analysis the four-dimensional spectral matrix. The results show that, in the range of 0~20 salinity, the fluorescence intensity of jet fuel decreases first and then increases, but the fluorescence intensity of lube increases first and then decreases. The analytical spectral curves of the mixed oils are in good agreement with the actual spectral curves of the jet fuel and lube. The recovery rate of jet fuel obtained by AWRCQLD algorithm is 100.2%~109% and the root mean square error is 0.002 1 mg·mL-1; the recovery rate of lube is 91.8%~109.3% and the root mean square error is 0.004 8 mg·mL-1. By introducing the salinity of seawater as a new dimension of data, the three-dimensional spectral data array is superimposed on this dimension to obtain the four-dimensional spectral data array. In this paper, the four-dimensional spectral data matrix is analyzed by the AWRCQLD algorithm. The purpose of qualitative and quantitative analysis of mixed oil under different salinity conditions is achieved. At the same time, this paper provides a reference for detecting petroleum mixed oil under different salinity conditions.