Aiming at the problem of low accuracy of the traditional load calibration equation for calculating wing skin load, a novel method of wing skin load calculation based on deep learning was proposed. Considering that the force of the real wing skin was complicated, this paper established a simplified wing structure model. Firstly, the finite element analysis on the wing was carried out by using Ansys software to obtain the strain and force simulation data, then the simulation data was cleaned and preprocessed. Secondly, a deep neural network model was constructed, its input and output were the strain and load values, respectively. The Adam optimization algorithm was used to optimize the model for load calculation. Finally, the test set was used to predict the load value, and the average relative error and absolute error were used as evaluation metrics. Experimental results show that the calculation method based on deep learning obtains the average absolute error of 0. 081 N for small load data and average relative error of 0. 063 8% for normal load data, respectively. The load accuracy of new method is obviously better than that of the traditional method comparing with traditional load calibration method.