## 1 Introduction  
As performance requirements for aircraft continue to escalate, the structures of these aircraft are subjected to increasingly extreme mechanical environments [1]. Consequently, the structural health monitoring and maintenance of aircraft are of critical importance. Currently, structural health monitoring mainly focuses on the wings and key areas associated with normal accelerations and loads based on the controlled maintenance costs [2]. Methods for studying those accelerations or loads mainly include the theoretical, direct and indirect methods [3]. In engineering, indirect methods are the most widely used and technically mature approach. The most common indirect method is based on strain gauge load measurements, which involve calculating key loads using measured strain data and stiffness matrices [4]. [PERSON] et al. [5] were the first to propose the vision of applying this method to the structural health monitoring system of the F-35 aircraft. [PERSON] et al. [6] further emphasized the necessity of implementing this method for health monitoring of this aircraft model. [PERSON] et al. [7] provided a detailed description of the method, which includes the use of load equations and strain sensor measurements to determine external loads; strain measurements are primarily employed to establish, calibrate, and optimize these load equations. Research on the use of fiber Bragg grating (FBG) sensors for strain collection has become highly advanced. [PERSON] et al. [8] have conducted an in-depth study of these sensors, characterizing and quantifying the strain transfer mechanisms and measurement accuracy in composite structures. However, traditional methods encounter issues with both efficiency and accuracy. First, in terms of accuracy, these conventional methods do not classify the maneuvers, and as a result, the accuracy of load analysis cannot be further improved [9]. The recognition and classification of aircraft maneuvers, also known as FMR, facilitate the establishment of distinct calculation models for training and predicting flight parameter data with different characteristics, thereby significantly enhancing accuracy [10]. Secondly, in terms of efficiency, both experimental measurements of strain and computational simulations involve high computational costs, resulting in low analytical efficiency. Currently, neural network models based on deep learning offer high research value by training on existing data to establish alternative models [11; 12]. Although, one problem is that a high number of features in a dataset may lead to dimensional disaster for deep learning methods, increasing computational complexity, and leading to overfitting in some machine learning models [13]. For example, [PERSON] et al. [14] described the data recording system of the F-22, which captures a comprehensive set of 670 flight parameters. This dataset encompasses 219 state parameters, including normal overload, attitude angles, altitude, and velocity, alongside 451 switch parameters, such as fuel mass, landing gear status, and door positions, as depicted in Figure 1.  
The higher dimensionality of the flight parameter load strains makes it more difficult to build the prediction model, [PERSON] et al. [15] suggested that the dimensionality of the obtained flight parameters should be reduced while retaining some important dimensions. Common dimensionality reduction methods include Principal Component Analysis (PCA), AE, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Locally Linear Embedding (LLE) [16], where PCA belongs to linear dimensionality reduction methods [17], while AE, t-SNE, and LLE belong to nonlinear dimensionality reduction methods [18]. Linear dimensionality reduction methods assume that data can be represented by linear transformations during the dimensionality reduction process, where the reduced features are linear combinations of the original features. PCA achieves dimensionality reduction by finding the directions of maximum variance in the data and projecting the data onto these directions [19].