Aiming at the problem of zero bias error of MEMS gyroscope caused by temperature change, a zero bias compensation method based on particle swarm optimization (PSO) and radial basis function (RBF) neural network was proposed. RBF neural network was used to build the model of the temperature error of gyro zero bias after pretreatment. After searching the optimal parameters of RBF neural network with PSO to improve its generalization ability, the optimal parameters of PSO-RBF neural network are used to compensate zero bias of gyro. Experimental results demonstrate the effectiveness of the proposed algorithm. After the compensation by PSO-RBF neural network algorithm, the maximum error of MEMS gyroscope zero bias decreases from 0.046(°)/s to 0.0034(°)/s, and the standard deviation decreases from 0.0427(°)/s to 0.0013(°)/s, which can effectively improve the stability of zero bias of MEMS gyroscope.