## 4 Experiment and Result Analysis
### Experimental Results and Analysis  
This article tested the macro average accuracy P, macro average recall R, and macro average F1 values of each model on the Winter Olympics Chinese question data test set, as shown in Table 6.  
Figure 5: The impact of different parameters on indicator values. By comparing the performances of the different models on the multi-intent recognition task for the Winter Olympics problem in Table 6, we were able to gain a deeper understanding of their performances, strengths, and limitations. First, we used Bert as the baseline model, which performed well on the Macro_P value (98.40%) and Macro_F1 value (95.51%), highlighting [PERSON]'s superior ability to deal with multi-intent problems. Further comparison experiments showed that the introduction of both TextCNN and BiLSTM-Multi-heads attention models alone could effectively improve the performance of the multi-intent recognition model, resulting in improvements of 0.96% and 1.14% in the Macro_F1 value, respectively. However, our in-depth analysis revealed that this enhancement effect may have been affected by the characteristics of the problem dataset in real tasks. Considering the characteristics of the problem dataset with longer text and more contextual associations, we paid special attention to the performances of the TextCNN and BiLSTM-Multi-heads attention models introduced separately. The TextCNN model focuses more on the local key features in text features, which is suitable for dealing with short texts and situations where local features are more important. However, in dealing with long texts and multi-intent recognition tasks with more contextual relevance, TextCNN may be limited by its local attention property, resulting in a relatively weak performance in capturing overall contextual features. In contrast, the BiLSTM-Multi-heads attention model performs better in this case, as it is good at capturing the semantic features of long-range text and more suitable for processing tasks with a high contextual relevance. In terms of model blending, by combining the two models, BiLSTM-Multi-heads attention and TextCNN, the BCNBLMATT model improved the Macro_P, Macro_R, and Macro_F1 values by 1.2%, 2.52%, and 2.61%, respectively, compared to the single model. This confirms the superiority of multi-model fusion, which makes full use of the advantages of each model to extract the semantic features of the problem text more comprehensively, thus improving the overall performance. We further validated the effectiveness of the Multi-head Attention mechanism in the BCNBLMATT model. Compared to the regular Attention mechanism, the Multi-head Attention mechanism improved the Macro_F1 value by 1.9%. This provides empirical support for the choice of the Attention mechanism, especially the effectiveness of Multi-head Attention in dealing with multi-intention problems. This result re-emphasizes the importance of selecting Multi-intent Attention mechanisms when solving complex tasks. In the results of the comparative experiments on the MixATIS dataset, we first focused on the performance of Bert as a benchmark model. Even in the multi-intent recognition task in different domains, Bert still showed an excellent level of performance. Its performance in terms of the Macro_P, Macro_R, and Macro_F1 values reached 97.62%, 94.74%, and 95.78%,