This paper proposes a novel method of compensating for the fiber optic gyroscope (FOG) temperature drift at full temperatures: the temperature field inside the NFS is constructed by multiple temperature variables, which are composed of the thermometer information built in the three inertial sensors, and then the support vector regression (SVR) is used to describe the relationship between the multiple temperature variables and the temperature drift error of the FOG, and finally the sparrow search algorithm (SSA) is applied to tune the model parameters to improve the accuracy and generalization capability. The experimental results validate the effectiveness of the proposed method, and we improve the accuracy of the NFS start-up stage from 0.0209° to 0.0101°. The performance is closely comparable to that of the stable stage, and improves the fast response capability of NFS at different initial temperatures.