Phenol, thymol and other phenolic compounds seriously harm the human body, animals and plants. They often exist in water at the same time. Because the excitation emission spectra of phenol and thymol are overlapped severely, and the conventional fluorescence method cannot achieve direct and rapid determination. Identification and quantification of two phenolic compounds (phenol and thymol) in lake water with unknown interferences using the three-dimensional fluorescence spectroscopy combined with four- dimensional parallel factor (4-PARAFAC) algorithm. The spectral data were analyzed and processed by the three-way parallel factor and the four-way parallel factor algorithm to explore the “high-order advantage” of the third-order correction algorithm. In this paper, the method of introducing an extra solvent mode to construct four-way data set was firstly used. The four-dimensional data array was obtained by superimposing the excitation emission matrixs obtained by scanning at different temperatures along the temperature dimension. Identification and quantification of two analytes using third-order calibration method based on four-way PARAFAC coupled with four-dimensional data. In order to avoid the influence of the instrument and scattering of solvent, firstly, the data obtained by scanning was preprocessed. The scattering signal in the excitation emission matrixs was removed by subtracting the blank sample and Delaunay trigonometric interpolation, and further excitation emission correction was performed to obtain true spectra. Then the data were analyzed using the second-order calibration algorithm based on the parallel factor and the third-order calibration algorithm based on the four-dimensional parallel factor, and the analysis results of two algorithms were compared. The results showed that the four-dimensional data array does not stimulate the simple superposition of emission matrices, and the four-dimensional data have rich high-dimensional information, which helps to improve the measurement results of the analytes. The average recoveries were obtained by the four-way parallel factor analysis algorithm, being the results: 97.7%±9.2% for phenol and 96.5%±8.8% for thymol. And the root-mean-square error of prediction values, for phenol and thymol, were 0.047 and 0.057 μg·mL-1, and the values of prediction relative error were less than 10%. Which were better than the results of three-way parallel factor analysis (105.7%±15.3% for phenol and 111.0%±3.6% for thymol, and the root-mean-square error of prediction values, for phenol and thymol, were 0.090 and 0.056 μg·mL-1, and the values of prediction relative error were larger than 10%). In short, the third-order calibration algorithm was superior to the second-order calibration algorithm in the sample with high background interference and severe collinearity. It provides a reliable method for the determination of phenol and thymol in the complex system.