To accomplish the near-infrared spectroscopy detection of wood moisture content under an unstable temperature environment, the change law of wood near-infrared spectroscopy under different temperatures and the influence of temperature changes on the near-infrared prediction of wood moisture content were explored. Using 75 pieces of log samples of Pinus sylvestris, Fraxinus mandshurica, Populus sylvestris, and Korean pine logs collected from the forest farm, a total of 300 pieces of samples were used to conduct near-infrared spectroscopy under different temperature and moisture content conditions. The correction set at a single temperature was used to establish a partial least squares moisture content prediction model with the verification set at each temperature. The influence of temperature changes on the prediction accuracy of the wood moisture content model was explored. The global models of wood moisture content prediction temperature based on different spectral pretreatments are compared. Collect infrared spectroscopy data at different temperatures under the same moisture content, and perform spectral averaging, differential observation, principal component analysis, and partial least square discriminant analysis on the spectra to explore the law of wood near-infrared spectroscopy changes with temperature. The results show: (1) Temperature significantly affects the spectrum of wood samples. Principal component analysis and discriminant analysis show that samples at different temperatures have a clear clustering trend, and the accuracy of temperature discrimination is 96.1%. The temperature will affect the position and absorbance of the absorption peak at a specific wavelength in the near-infrared spectrum of wood. With the same moisture content, as the temperature increases, the absorption peak at a specific location tends to-shift to the high-frequency band gradually and is at a sub-zero temperature when the peak movement changes more obviously. (2) The PLS moisture content prediction model at different temperatures has different adaptability to temperature changes. The wood moisture content prediction model is more suitable for detecting samples at the same temperature as the modeling sample. Compared with the single temperature model, the PLS temperature global model has good adaptability and application potential for temperature changes, and the RMSEP is best at 0.082. The PLS water content temperature global model based on SG smoothing + multivariate scattering correction + first-order derivative preprocessing has a better prediction effect and temperature adaptability, and the RMSEP is reduced to 0.088. It can be seen that temperature variation is a disturbance factor that cannot be ignored in detecting wood moisture content by the near-infrared method. The global temperature model based on spectral pretreatment can significantly improve the temperature applicability. This research can further promote the application of near-infrared spectroscopy technology in wood production and processing.