The influence of temperature change on the zero offset of optical fiber gyro is one of the key factors that restricting its performance.The BP neural network can improve the accuracy of temperature compensation to a certain extent,but the BP neural network has local minimum problem.In this paper,Ant Colony Optimization (ACO)BP neural network algorithm is used to compensate the drift of fiber optic gyro,and the initial parameters of BP neural network are optimized.The experimental results show that using ACO-BP neural network to compensate can improve the zero offset stability of fiber optic gyro by about 80% in the temperature range of -40 ℃~60 ℃,and the compensation effect is better than that of previous BP neural network.