【Objective】Pinus koraiensis needles exhibit a significant forest fire risk due to their high oil content, and surface fire spread is the main fire spread mode. Developing a predictive model for surface fire spread rates can provide scientific basis and valuable insights for fire prevention and control in Pinus koraiensis plantations.【Method】The dead coniferous layer of Pinus koraiensis plantation in Liangshui area of Heilongjiang province was used as the material, 360 sets of indoor point burning tests were conducted with water content of 0, 5%, 10%, 15%, 20%, slope of 0°, 5°, 10°, 15° and wind speed of 0, 1, 2, 3, 4 and 5 m/s. Based on the fire spread rate measured by thermocouple method, an improved WOA(IWOA)-BP neural network model was constructed to predict the fire spread rate, and the prediction results were compared with those of three models (WOA-BP neural network, GA-BP neural network and PSO-BP neural network).【Result】Slope, wind speed and fire spread rate were significantly positively correlated (P<0.01), while water content exhibited a negative correlation with fire spread rate (P<0.05). The fire spread rate decreased with an increase in fuel water content, and increased with the increase of wind speed and slope. When the wind speed was 4 m/s, the fire spread growth rate reached the maximum. The improved whale optimization algorithm (IWOA) included Tent chaotic mapping, improved nonlinear convergence factor, adaptive weighting and Levy flight motion. These enhancements increased the algorithm’s randomness and diversity, thereby improving its convergence speed and reducing the likelihood of becoming trapped in local optima, with high prediction accuracy and robustness. The accuracy and stability of the BP neural network model optimized by the IWOA demonstrated significant improvements compared to three other models, exhibiting the highest model fitness to the measured data.【Conclusion】The IWOA-BP neural network model can effectively predict the fire spread rate of the dead coniferous layer of the Pinus koraiensis plantation, and providing scientific guidance for forest fire prevention and control and forest litter fire spread rate prediction model.