## 3 Results
### Model Performance Comparison  
To test whether the feature extraction method proposed in this paper can provide high-quality input to the capability evaluation model, i.e., whether it can effectively improve the accuracy of capability evaluation, the extraction results of this method are compared with those of the Principal Component Analysis (PCA) method [26], the Common Principal Component Analysis (CPCA) [27] method, and the random forest method (SVM) [28]. Since the feature extraction results provide an efficient set of features for capability evaluation, to guarantee the comparability of the four methods, the KNN query is used to test the effect of feature extraction on the classification of capability evaluation. In the field of capability evaluation, PCA-SVM, CPCA-SVM, and CPCA-RF represent a spectrum of commonly used feature extraction techniques. PCA-SVM and CPCA-SVM are dimensionality reduction and classification algorithms that are based on the combination of Principal Component Analysis and Support Vector Machines. CPCA-RF is a variant of the PCA method that incorporates classification by random forest. By comparing our method to these well-established approaches, we aimed to demonstrate the efficacy and potential benefits of our RNN autoencoder-based method for extracting relevant features and enhancing classification performance. The specific process is as follows. In the first step, the features of each object in the sample set S are extracted using PCA-SVM, CPCA-SVM, CPCA-RF, and this paper's method (RNN autoencoder), respectively. The original sample set S is mapped to the feature space to construct SA. In the second step, the dynamic time-bending distance (DTW) algorithm is used to find the K closest samples for each sample in SA, respectively. In the third step, the accuracy of various methods for the sample K-nearest neighbor finding is counted. The four methods extracted the first three features separately in order to ensure the consistency of feature extraction during the accuracy comparison. The final calculated model accuracy results are shown in Figure 7.  
The experimental results of comparing the feature extraction method proposed in this paper with the PCA-SVM method, CPCA-SVM method, and CPCA-RF method are presented in Figure 7. To ensure the comparability of the four methods, the KNN query was used to test the effect of feature extraction on the classification of capability evaluation. The results show that the RNN autoencoder proposed in this paper achieves the highest K-nearest neighbor finding accuracy of 0.65 at K equal to 1, outperforming the other four algorithms. Additionally, the proposed method consistently maintains an accuracy above 0.5 as the value of K increases. While the CPCA-RF algorithm and CPCA-SVM algorithm perform better at K equal to 1 and 5, their performance decays more quickly as K increases to 10. Although the overall classification accuracy of the four methods is not high, the proposed feature extraction method outperforms the other three methods in classifying ability subjects using KNN. These results demonstrate that the proposed feature extraction method is effective in providing high-quality input to the capability evaluation model and improving the accuracy of capability evaluation. We employed the Support Vector Machine (SVM) as an alternative classification method to compare model performance. The Support Vector Machine is a widely used classification algorithm applicable to both linear and nonlinear classification problems. We compared the SVM classification performance based on the RNN autoencoder, PCA, CPCA, and SVM feature extraction methods. The specific process is as follows. In the first step, the features of each object in the sample set S were extracted using PCA-SVM, CPCA-SVM, CPCA-RF, and the method proposed in this study (RNN autoencoder). The original sample set S was mapped to the feature space, constructing SA. In the second step, SA was divided into a training set and a test set. The SVM classifier was trained using the training set.