Aiming at the problem that the pointing accuracy of star tracker bracket is reduced due to thermal deformation, a pointing inclination monitoring method based on neural network is proposed. Firstly, the structural characteristics of the star tracker bracket are analyzed, the pointing inclination prediction system of the star tracker bracket is built, the structural deformation and inclination change data of the star tracker bracket are collected, and the experimental data are preprocessed. Secondly, the depth neural network model is constructed, the strain information of each measurement point of the star tracker bracket model is taken as the input variable, and the network parameters are updated by Adam optimization algorithm. After training iteration, the pointing inclination prediction model is obtained. Then, aiming at the limitation of slow convergence and easy to produce local minimum value of traditional deep neural network, genetic algorithm is used to optimize the hyperparameters of deep neural network to improve the training efficiency. Finally, the test set data is used to predict the change of the pointing angle of the star tracker bracket, and the accuracy of the model in monitoring the pointing angle of the star tracker bracket under different temperature conditions is analyzed. The experimental results show that the average error of the directional inclination prediction method of the optimized depth neural network model is 0. 20" , and the accuracy of inclination prediction is significantly better than the traditional algorithm, which proves that the deep learning method is feasible to realize the directional inclination monitoring of star tracker bracket.