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【Objective】To improve the linear fit between the reflected spectral center wavelength and external environmental variables in Fiber Bragg Grating (FBG) sensing system, this paper proposes to use particle swarm optimization of Gaussian process regression model to the field of FBG stress sensing.【Methods】For the reflectance spectral characteristics of the FBG, the paper studies the impact of the linear fit in the spectral fitting of FBG sensing system. The particle swarm algorithm is used to search for the optimal hyperparameters in the Gaussian process regression model in order to enhance the predictive performance of the reflectance spectral wavelength of the center. A FBG stress sensing experimental platform was built, and the FBG was laid on the strength beam. Different weights were applied to one end of the equal strength beam to produce axial strain on the FBG, and the reflectance spectral data were collected by the spectrometer and analyzed by linear fitting with the studied model. The results obtained by the unoptimized Gaussian process regression model, the maximum value method, the Gaussian fitting method, and the center of mass method were used as the control group.【Results】The results show that under the conditions of erbium-doped fiber amplifier output power of 10 dBm, transmission fiber distance of 50 m, and the number of sampling points of the spectrometer of 501, the linear fit between the reflected spectral center wavelength and the mass of the weights is better than that of the control group. The linear fit of the studied model can reach up to 0.951 9, which is improved compared with that of the control group. Under the conditions of 501, 251, 167 and 126 spectral sampling points, the studied model can improve the linear fit of the system to 0.990 0, which is a maximum improvement of 0.258 7 compared with the maximum value method.【Conclusion】The analysis results show that the Gaussian process regression model optimized by the particle swarm is able to effectively improve the linear fit of the FBG stress sensing system.