## 1 Introduction
In another work [15], GLCM was adopted for feature extraction from plant leaf images, leading to excellent classification results using deep convolutional neural networks. However, when the dimensionality of extracted features becomes high, feature selection techniques are needed to eliminate irrelevant or redundant features and improve model performance. In [16], a feature importance recursive elimination method combining support vector machines, random forests, and generalized boosted regression algorithms was used to select the optimal feature subset, yielding promising results. In [17], the authors applied XGBoost for denoising and dimensionality reduction of biological-related features, leading to the development of the StackPPI model, which improved the accuracy of protein-protein interaction prediction. In [18], an approach based on mutual information coupled with bare-bones particle swarm optimization (BBPSO) was utilized to achieve superior feature subset selection performance through effective population initialization, local search operations, and adaptive mutation operations. To summarize, in this paper we introduce a one-dimensional convolutional approach that relies on feature fusion for predicting the content of rare earth elements. Initially, machine vision technology is used to extract the RGB components of the Pr/Nd solution image color space, which is then converted into HSV and YUV color spaces. The primary components of these two color spaces are extracted to serve as the image's color features. Following this, the Gray Level Co-occurrence Matrix (GLCM) is used to extract seven features from the Pr/Nd solution image, namely, the energy, correlation, contrast, homogeneity, autocorrelation, and matrix mean, which function as the image's spatial texture features. Considering that in real-world rare earth extraction processes the content value of rare earth element components across different extraction stages is often strongly correlated with the extraction stage, the corresponding extraction stage in the image is selected as a crucial feature. The composite features of the rare earth solution are derived using a feature recursive elimination method based on a random forest. For these mixed features, we paper propose a one-dimensional convolutional neural network prediction model with multiple residual attention blocks (MRAB-DNN). This model incorporates a residual attention block (RAB) structure, enabling the model to diminish the weight proportion of noise through the attention mechanism while extracting deep features, facilitating rapid convergence. Through experimental validation using Pr/Nd element data in the field, the method presented in this paper can be applied to the real-time detection of rare earth element components at extraction production sites. The structure of this paper is as follows. Section 2 describes the color feature extraction of rare earth solution and introduces several spatial texture feature extraction methods. In Section 3, we introduce the Random Forest model used to obtain the optimal feature set. Section 4 introduces the one-dimensional residual attention convolutional neural network model used to predict the component content of the rare earth elements. Section 5 reports the experimental results and provides analysis. Finally, the paper is concluded in Section 6.