## 1 Introduction  
The flight load refers to the load borne by the aircraft during the entire flight process from takeoff to landing. The main purpose of conducting flight load analysis is to identify the most severe load conditions for different aircraft components in different flight states [1]. The results of flight load analysis show the magnitude and distribution of the load on the aircraft structure, which is an important basis for aircraft structural strength design. In recent years, with the extensive use of composite materials in civil aircraft, it has brought benefits of weight reduction to the aircraft, but also posed new requirements for strength and structural design [2]. Therefore, flight load calculation and analysis is an important part of civil aircraft design. During flight, an aircraft is subjected to a variety of external forces, such as aerodynamic, gravitational, and inertial forces. These forces can vary significantly depending on factors such as airspeed, altitude, maneuvering, and atmospheric conditions. Flight load analysis aims to determine the magnitudes and distributions of these forces on different parts of the aircraft, including the fuselage, wings, tails, and landing gear. Traditional flight load design has been well developed and has achieved many results, including engineering design examples and scientific research papers. In engineering applications, different flight parameters, including airspeed, altitude, and maneuvering capabilities, are selected based on the aircraft model to construct flight envelopes. Then,CFD calculations [3; 4] or wind tunnel calculations [5] are performed on the component loads within the flight envelope to establish a component load database. The load distribution of the components and the entire aircraft is determined based on static aeroelastic analysis, and the severe load conditions of the components are selected as important basis for aircraft structural strength design. Due to the large number of flight load calculations that can reach the millions, the computational resources required for flight load analysis are enormous, and the cycle of analysis is very long. In order to solve this problem, various methods have been developed, such as low-order panel methods and high-order panel methods. However, each method has its own obvious drawbacks. The low-order panel method [6] provides two-dimensional results with low accuracy, and it is only suitable for the early design stage of the aircraft. On the other hand, although the high-order panel method [7; 8] has guaranteed accuracy and significantly reduces the computation time compared to CFD-CSD coupled calculations, it still requires significant computational resources for millions of aircraft load calculations. In this case, surrogate models based on numerical regression and neural networks have made a lot of progress and achievements in reducing the flight load calculation process. [PERSON] et al. demonstrate the potential of deep learning-based regression models in improving efficiency and accuracy of aircraft structural design for flight loads calculations [9]. [PERSON] et al. introduce a novel alternative reduced order model that improves the accuracy of predicting loads induced by aircraft lateral oscillation by 150% compared to a fixed fuel mass model [10]. [PERSON] et al. utilize cluster Kriging and machine learning techniques to accurately represent the complex physical relationships of aeroelastic responses, resulting in significant vibration reduction during high-speed flights for composite rotor blades [11]. These studies demonstrate that flight load surrogate model techniques simplify the cumbersome traditional flight load calculation methods and have significant potential for application [12]. By contrast, the aerodynamic load analysis based on feature value analysis and partial least squares regression remains a blank space at present.