In wind tunnel experiments， sensors are normally placed on wing surfaces to measure the aerodynamic load at the corresponding positions. Due to the limited number of the placed sensors， it is difficult to directly obtain the holographic aerodynamic load distribution of the whole wing. In this paper， we use machine learning methods to reconstruct the holographic aerodynamic load on the wing surface from the limited sensor data， and propose a method to optimize the placement of sensors using the simulation data. We select limited position data from the wing holographic aerodynamic data calculated by computational fluid dynamics（CFD） to simulate the sensor experimental data， and compare the reconstruction accuracy of the deep learning model， Gaussian process regression（GPR）， support vector regression（SVR） and BP neural network（NN） to the aerodynamic load. The sensor layout is optimized by evaluating the holographic load accuracy reconstructed by the sensor data. The M6 wing is taken as an example to verify the proposed method under two given working conditions. The experimental results show that the GPR model achieves the best accuracy of aerodynamic load reconstruction. The optimal section number and single section number of M6 wing under different total number of sensors， the optimal arrangement under the lowest number of sensors， and the arrangement of sensors in the leading edge area and spanwise section where the flow field changes greatly are all given.