Based on the fiber optic gyroscope, an angular motion integrated measurement sensor can achieve integrated and dynamic precision measurement of various angular motion parameters. However, in practical applications, the fiber optic gyroscope is susceptible to temperature changes, leading to a decrease in measurement accuracy. Addressing this issue, this paper proposes a temperature error compensation technique for the angular motion integrated measurement sensor based on an adaptive wavelet echo state neural network. To advance the progress of temperature error modeling and enhance the approximation capability of traditional neural networks, an adaptive forward linear prediction filter is applied to preprocess temperature drift data from the gyroscopes used for modeling. The paper adopts an adaptive wavelet echo state neural network to establish a temperature drift model, aiming to avoid issues such as the blind design of traditional neural network structures and local optima. This approach enhances the network's learning and generalization abilities. Additionally, an adaptive law is employed to replace neural network gradients during network training, thereby improving the approximation accuracy and convergence speed of the neural network. Experimental results demonstrate that the proposed model can enhance the measurement accuracy and environmental adaptability of angular motion integrated measurement sensor, providing reliable technical support for the performance optimization and practical applications of these sensors.