## 5 Experiment and Result Analysis
### Model Comparison  
To validate the efficacy of the MRAB-CNN model, we initially utilize mixed features as datasets. During the experimental process, we established a variety of prediction models and compared them using identical test and training data. By adjusting the model's parameters and configuration, it was possible to achieve an optimal state. Subsequently,  
Figure 9: Model training loss curve change diagram. through comparative experiments, we evaluated and compared the performance of these models on the same dataset. The corresponding parameters of each model are as follows:  
1. Genetic Algorithm optimized Extreme Learning Machine (GA-ELM). The Genetic Algorithm was configured with a population size of 20 and ran for 100 iterations. The crossover probability was set to 0.01, while the mutation probability was set at 0.7. The Extreme Learning Machine was configured with 25 nodes. 2. Least Squares Support Vector Machine (LSSVM). This model employs the radial basis kernel function (rbf). The penalty factor (C) was set at 70 and the kernel function coefficient (gamma) was 0.11. 3. Xgboost model. The model was configured with 84 decision trees (n,estimators). The learning rate was set to 0.205, while the maximum depth of each decision tree (max_depth) was 15. The minimum sample weight of the child nodes (min_child,weight) was set to 8, and the parameter of the L2 regularization term (lambda) was set to 3.31. 4. RAB-DNN. This model has an attention layer with eight channels and contains three residual attention units. The number of channels was set to 16, 32, and 64, respectively, across the layers. The fully connected layer of the model was configured with 256 neurons. The aforementioned models were individually trained and used for predictions. Evaluation indices such as MeanRE, MaxRE, RMSE, and R2 were employed to obtain a comparison of each model, as depicted in Table 2. Simultaneously, the relative error percentage results of each model's predicted values were graphed, as illustrated in Figure 10.  
As per the data presented in Table 2, the GA-ELM model exhibits commendable performance among the machine learning models discussed above, with an RMSE of merely 0.7978. However, the prediction accuracy of the MRAB-CNN model surpasses that of the GA-ELM model by 44.3%, indicating its superior performance. As evidenced by Table 2 and Figure 10, the MRAB-CNN model proposed in this paper outperforms other models in terms of MeanRE, MaxRE, RMSE, and R2 evaluation indicators. The relative error percentage curve of the model exhibits minimal fluctuation. The overall performance of the improved RAB-DNN model surpasses that of other machine learning models. This demonstrates the effectiveness of the one-dimensional convolution model based on multiple residual attention blocks proposed in this paper for predicting the content of rare earth elements. Simultaneously, a comparison of the optimal values of this model with those of other models reveals that the average relative error of the MRAB-DNN model is 0.4465% lower than that of the GA-ELM model. The maximum relative error is 1.3741 lower than that of the Xgboost model, the root mean square error is 0.2882 lower than that of the RAB-CNN model, and the coefficient of determination is 0.00041 higher than that of the RAB-CNN model. Furthermore, the maximum relative error of this method is within 3%, which is less than the 5% requirement for component content detection error set by rare earth extraction and separation enterprises, thereby meeting their detection requirements. In conclusion, the one-dimensional convolution prediction model based on the residual attention block proposed in this paper exhibits strong comprehensive performance and can be applied to the detection of component content in rare earth extraction production sites.