## 1 Introduction  
As an important raw material for production, wood plays a significant role in many aspects of daily life, such as the manufacture of furniture and the construction industry. Currently, there is a shortage of forest resources in China; therefore, it is crucial to use wood resources rationally and efficiently; mechanical strength testing of wood is a crucial aspect of wood resource utilization. The mechanical strength of wood mainly includes compressive strength, tensile strength, bending strength, hardness, etc. Current research focuses primarily on bending strength, while relatively little research has been reported on compressive strength, etc. [1]. The traditional method for determining the mechanical strength of wood requires destructive testing on wood specimens, which is expensive and causes a great waste of resources. Coniferous wood has a regular structure and a soft composition, making it an essential mechanical wood. Therefore, it is essential to develop an effective and nondestructive method for testing the mechanical strength of coniferous wood [2, 3]. Commonly used non-destructive testing methods for wood are ultrasonic testing methods [4], stress wave detection method [5], X-ray inspection method [6], near-infrared spectroscopy [7], etc. In recent years, near-infrared spectroscopy (NIRS) methods have received an increasing amount of attention and research due to their ease of operation, safety, and environmental protection [8]. NIRS reveals the structure and composition information of organic matter by using the principle that the NIR absorption wavelength and intensity of different groups or the same group in different chemical environments are significantly different [9]. Researchers have made a number of advances in the non-destructive testing of the mechanical strength of wood using NIR spectroscopy. [PERSON] et al. [10] used NIR spectroscopy to predict the density, modulus of elasticity (MOE), and modulus of rupture (MOR) of two coniferous woods. They also investigated the effects of wood surface roughness, infrared spectral range, and machine learning model on the prediction models. The results demonstrated that the prediction accuracy of NIRS data obtained from rough surfaces was higher, and the proposed adaptive neuro-fuzzy inference system (ANFIS) had better prediction accuracy on MOE and MOR compared with multilayer perceptron (MLP) neural network (NN) and partial least squares (PLS) models. [PERSON] et al. [11] collected chestnut wood samples from three different species sources in Europe, preprocessed the raw spectra, constructed a regression model, and applied variable selection techniques to enhance the model performance; finally, the root-mean-squares error of cross-validation (RMSECV) of the MOE regression model was 696.01 MPa, and R\({}^{2}\) was 0.78. [PERSON] et al. [12] extracted the characteristic wavelengths closely associated with the Mongolian oak MOE using a cooperative interval partial least squares and step-by-step projection algorithm and used a back propagation neural network (BPNN) to construct a calibration model with correlation coefficients of prediction (RP) reaching 0.91 and the root mean square error of prediction (RMSEP) reaching 0.76 MPa. In recent years, with the rapid development of technologies such as artificial intelligence, big data, and cloud computing, new chemometric methods for spectral analysis have become the center of attention and a popular topic among researchers [13]. As one of the preferred research methods, deep learning methods are gradually applied to the field of NIR analysis [14]. [PERSON] [15] applies deep learning-stacked sparse autoencoder (SSAE) methods to extract the advanced features of the NIR spectroscopy, and then builds the prediction model using the affine transformation (AT) and the extreme learning machine (ELM). The results show that the proposed method is superior to other typical NIR analysis methods. [PERSON] et al.