The methane (CH4) gas released by hydrothermal enters into the ocean and atmosphere successively by diffusing and causes inestimable effect on earth in physics, chemistry and biology. The principle and environment effect of abyssal hydrothermal still require further study because limited information is available about dissolved methane. In our previous work, we propose an optical passive imaging interference system (OPIIS) for the real-time detection and long-term observation of hydrothermal methane’s concentration, temperature, and pressure. To accurately, stably, and rapidly obtain the information of hydrothermal methane from OPIIS’s interferogram, this paper processes OPIIS’s data by combining interference spectra and partial least squares (PLS) algorithm. We built three single-dependent variable models between methane radiance spectra and gas concentration, temperature and pressure, respectively. Then we can establish the PLS prediction model between interference fringes indirectly on the basis of relationship between interference fringes and radiance spectra, which can improve the capacity of resisting disturbance and stability of prediction models in practical application. On the basis of Lorentz profile, we build the deep ocean gas emission model different from atmosphere emission and obtain the synthetic methane radiance spectrum database at any concentration, temperature and pressure by using the methane spectral parameters from HITRAN2016 molecular spectroscopy database. The six spectral lines of methane in the range of 1.64～1.66 μm are selected for the PLS regression model between methane radiance spectra and gas concentration, temperature and pressure. Furthermore, this paper analyzes the contribution of number of training samples, interval of training samples and number of principal components to the improvement of the comprehensive performance of regression model. The 96 groups of concentration, temperature and pressure regression model are built by using different groups, intervals and principal components, and those regression models are cross-validated using 25 groups of prediction samples. The comparison results of those regression models’ root mean square error of prediction (RMSEP) and coefficient of determination (R2) indicate that the change of single factors such as the number of training samples, the interval of training samples and the number of principal components can not improve the prediction model’s comprehensive performance about prediction accuracy, stability, application scope and computation. Finally, the optimized model with balanced performance is determined with concentration, temperature and pressure application ranges at 5～375 mmol·L-1, 580～678 K, 10～34.5 MPa, training samples of concentration, temperature and pressure are 50 groups, 25 groups, 25 groups, intervals at 5 mmol·L-1, 2 K, 0.5 MPa, principal components are 2, 2, 5. The RMSEPs of concentration, temperature and pressure are 3.082×10-6, 0.977 0, 5.052×10-3, and R2s are 0.999 9, 0.998 9, 0.999 9, respectively. The prediction errors of concentration, temperature and pressure are ±1.21×10-7, ±3.63×10-3, ±9.49×10-4, and the corresponding precisions are ±45.4 nmol·L-1, ±2.5 K, ±3.3×10-2 MPa. The results indicate that this retrieval algorithm can accurately, stably, and rapidly obtain concentration, temperature and pressure of hydrothermal methane.