Studies the modeling of gyro startup drift rate from acquired experimental gyro startup drift rate data and the nonlinear dynamic models of gyro startup drift rate related temperature established by time-delay neural network which enables the gyro temperature drift rate to be compensated in the process of startup and the gyro instant startup to be implemented. And introduces an improved genetic algorithm to learn the weights of neural network identifier to avoid stacking into the local minimal value and achieve rapid convergence.