The Miniature Inertial Measurement Unit (MIMU) is vulnerable to environmental influences, and has the shortcomings of non-linear output and low accuracy under severe angular and linear motion.Considering that the traditional polynomial calibration method is difficult to compensate for the dynamic error accurately, deep learning method was used to model and compensate for the overall dynamic error of MIMU.The shallow neural network and the deep recurrent neural network were used to establish the overall dynamic error model of MIMU.The calibration process based on three-axis temperature control position and rate turntable was designed, so that angular motion and temperature changes were applied to the three axes of the turntable of the inner, middle and outer axis simultaneously.An MIMU multi-factor influence error training set was also built.The experimental results showed that:1) The shallow neural network model has a slight improvement on the error compensation effect compared with the traditional model, and the deep recurrent neural network model can reduce the residual mean and mean square error significantly through compensation; 2) The effect of Gated Recurrent Unit (GRU) neural network model is the best, and there are fewer parameters to be trained and low computational burden.