To reduce the bias drift of the fiber optic gyroscope arising from the temperature effect and improve accuracy, a temperature compensation model of the fiber optic gyroscope was established based on the radial basis function (RBF) neural network model and particle swarm optimization (PSO-RBF). Temperature compensation tests were conducted on the three-axis fiber gyroscope in temperature environments of -40 to +60 ℃. The experimental results demonstrate that the model reduces the bias drift of the entire process of the fiber optic gyroscope by more than 85% under the condition of variable temperature, with prediction stability and compensation effect better than those of the traditional polynomial and unoptimized RBF models.