Spectral analysis technology has a certain potential in wood species identification, and mid-infrared spectroscopy technology is also widely used in qualitative and quantitative analysis. This research focuses on the identification of wood species by mid-infrared spectroscopy. Based on a deep convolutional neural network, an algorithm that combines cluster analysis (CA), symmetrical lattice image analysis (SDP) and deep learning (DenseNet) is proposed to achieve a high recognition rate with few parameters. With the advantages of DenseNet, the accuracy of wood recognition in mid-infrared spectroscopy is improved. First, 250 sets of mid-infrared spectroscopy data, including guaiacum sanctum, dalbergiabariensis, pterocarpuserinaceus, pterocarpusmacarocarpus, and spiraea, are collected. Through eliminating outliers based on Euclidean distance, the feasibility analysis of the remaining 240 groups as data to be analyzed and classified. The optimal parameters of SDP conversion are determined through the SDP conversion analysis of the original spectral data. The characteristics of original spectral data are filtered out through CA. According to CA, different thresholds determine the characteristics of the three groups of dimensions and related discussions are carried out. The optimal dimensional feature is initially determined by comparing the three sets of feature data, including the intra-class similarity and the inter-class difference between the images after SDP conversion. The determined optimal dimensional feature data is input into the SDP-DenseNet model to obtain model recognition accuracy. Finally, the comparative analysis verifies the validity of the model. On the one hand, the original data and the feature data of the other two sets of contrast dimensions are input into the SDP-DenseNet model to compare recognition accuracy. On the other hand, the optimal dimensional feature data is input into the random forest for recognition to compare the accuracy of traditional machine recognition and SDP-DenseNet algorithm recognition. According to the results, the accuracy of the SDP-DenseNet model filtered by the CA feature is generally higher than that of the SDP-DenseNet model directly input to the original data. The optimal dimension of CA feature selection is 255 dimensions, with the highest recognition rate of 88.67%. In the control group, the recognition rate of 107 dimensions is 77.78%, and the recognition rate of 322 dimensions is 68.89%. In contrast, the SDP-DenseNet model recognition rate of the original data is only 57.78%. The recognition rate of the random forest model corresponding to the optimal dimensionality data screened by clustering features is relatively low, only 66.67%. Therefore, the CA-SDP-DenseNet model proposed in this study can effectively improve the accuracy of mid-infrared spectroscopy in identifying wood species.