Inclination sensors are susceptible to measurement errors due to ambient temperature changes, namely temperature drift. Aiming at this problem, a temperature drift compensation model based on the improved genetic algorithm ( IGA) optimized back propagation neural network (BPNN) was designed. A new selection strategy and crossover mutation operator are used in the genetic algorithm, and a mechanism for jumping out of the local optimal solution is added. The experimental results show that the mean square error (MSE) of the IGABP compensation model is 0. 003 28, and the average temperature drift after the compensation model correction is 0. 039°, which is far better than the average temperature drift of 0. 190° without correction. The results show that, the IGABP compensation model has faster convergence speed and higher compensation accuracy compared with the traditional neural network model, which can effectively compensate the measurement error caused by temperature and improve the stability and accuracy of the inclination sensor.