## 3 Results and Discussion
### Comparative Analysis of She Ethnic Clothing Classification
#### 3.3.3 Comparative Analysis of Classification Models  
In order to validate the effectiveness of the classification models, this study conducted 10 classification experiments on CNN, SVM, BP, RNN, and RBF models. The test accuracy and average values obtained from these experiments are presented in Figure 7.  
From Figure 7, it can be observed that, in the 10 experiments, the average classification accuracy of SVM is 86.89%. This is because the SVM model uses non-linear planes for class separation, which leads to unsatisfactory classification results for She ethnic clothing classification. Due to the presence of a large amount of redundant information in the  
Figure 7: Classification accuracy of different models.  
training process, it is necessary to optimize the classifier performance by introducing deep learning networks [51]. Compared to the SVM, the classification accuracy of BP, RNN, and RBF neural network models were significantly improved, with average classification accuracy increased by 3.79%, 5%, and 7.57%, respectively. This indicates that neural networks have higher applicability in She ethnic clothing classification, especially in the fusion of color histograms and color moment features, where they exhibit better learning performance. However, the highest average classification accuracy achieved by the three aforementioned neural network models is only 94.46%.  
The FPA-CNN model proposed in this paper consistently achieved the highest classification accuracy in all 10 tests, reaching a maximum of 98.65%. The average classification accuracy also reached 98.38%, showing improvements of 11.49%, 7.7%,6.49%, and 3.92% compared to the corresponding results from the SVM, BP, RNN, and RBF models, respectively. The effectiveness of the proposed method in the She ethnic clothing classification is significant. This is because, compared to traditional classification algorithms, the proposed method employs receptive fields and weight-sharing techniques, reducing the number of weights required for training the network and the associated burden. Additionally, through supervised learning, the proposed method further enhances the information acquisition capabilities of the classification model, as a result of which the classification accuracy is effectively improved.  
To visually validate the effectiveness of the proposed method in She ethnic clothing feature classification, this study performed a kernel principal component analysis on the class features from the CNN, SVM, BP, RNN, and RBF classification results and simplified them into a two-dimensional plane. Based on this, comparative experiments were conducted using different classifiers, and the results are shown in Figure 8.  
Figure 8: Comparison of different classification methods and visualization. (**a**) SVM; (**b**) BP; (**c**) RNN; (**d**) RBF; (**e**) CNN.  
From Figure 8, we can see that the SVM, BP, RNN, and RBF all exhibit overlapping phenomena between two or more classes, resulting in certain classification errors. In comparison to the aforementioned classification methods, the proposed PFA-CNN method in this study demonstrates excellent performance in recognizing test samples. It achieves high classification accuracy for each category of clothing with minimal overlapping, and only a very small number of samples are incorrectly classified. Therefore, the FPA-CNN method proposed in this paper effectively classifies clothing from different regions of the She ethnic group and achieves higher classification accuracy than the SVM, BP, RNN, and RBF. This verifies that the proposed method has higher accuracy and efficiency.