## 5 Conclusions and Future Works  
This paper proposes an enhanced method for entity relation extraction in the Chinese domain, building upon the CasRel model. By introducing part-of-speech (POS) and pinyin features, the method effectively strengthens the model's ability to learn prior knowledge from Chinese texts, thereby enhancing its representation capabilities. Subsequently, the interactive fusion network and progressive contrastive learning strategy further optimize the integration of heterogeneous features, achieving a deep fusion of semantic, POS, and pinyin attributes. This leads to improved performance in complex Chinese text extraction scenarios. On the commonly used DuIE evaluation dataset for joint extraction, the proposed model achieved accuracy, recall, and F1 scores of 82.1%, 82.2%, and 82.2%, respectively. Compared to the baseline CasRel model, these represent improvements of 4.9%, 5.8%, and 5.4%, respectively. These results convincingly demonstrate the effectiveness of the proposed model in Chinese entity relation extraction tasks. Additionally, ablation experiments validate the critical role of POS, pinyin information, and the progressive contrastive learning mechanism in boosting model performance.  
However, the CJE-PCHF model still faces certain limitations in practical applications. Firstly, the POS and pinyin features are vulnerable to segmentation errors, inaccurate POS tagging, and pinyin ambiguities, which can undermine the effectiveness of feature extraction. Moreover, noisy data and unstructured text can also diminish the model's performance. Complexity analysis indicates that performance improvements often come at the cost of increased computational resources, which may become a bottleneck in resource-constrained scenarios, particularly when processing large-scale data. Future work should focus on exploring deeper Chinese character features, such as Wubi input and glyph structure, to enhance the model's comprehension of Chinese. Additionally, computational efficiency will be crucial in large-scale applications; thus, the model will be compressed through techniques like knowledge distillation, pruning, and quantization to reduce computational costs, optimize parallel processing, and improve inference speed. Furthermore, the model will be extended to more diverse and comprehensive datasets to evaluate its generalization capabilities.  
Conceptualization, Y.B. and M.H.; Methodology, M.H.; Software, M.H.; Writing--original draft, M.H.; Writing--review and editing, Y.B.; Supervision, D.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **CasRel** & **CJE-PCHF** & **Increment** \\ \hline The number of parameters & 111,792,482 & 112,151,138 & +0.32\% \\ Training time (ms) & 16.0 & 22.7 & +40.11\% \\ Inference time (ms) & 20.0 & 36.0 & +36.36\% \\ Runtime memory usage (MB) & 2378.6 & 2588.5 & +8.82\% \\ GPU memory used (MB) & 12,623.0 & 14,431.0 & +14.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Complexity analysis.  
**Funding:** This work was supported by the Inner Mongolia Autonomous Region Science and Technology Plan Project under Grant No. 2022 YFFHH0102 and No. 2021 GG0090, and the Basic Business Funding Project for Universities Directly under Inner Mongolia Autonomous Region under Grant No. BR220145.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: DUIE dataset: [[https://doi.org/10.1007/978-3-030-32236-6_72](https://doi.org/10.1007/978-3-030-32236-6_72)]([https://doi.org/10.1007/978-3-030-32236-6_72](https://doi.org/10.1007/978-3-030-32236-6_72)). The methodology and code of this study, if required, can be obtained by contacting the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.