## 5 Conclusions  
This study investigated the potential of CLTC, a critical task for transferring knowledge from high-resource to low-resource languages. Cross-lingual classification is inherently challenging due to the complex syntactic and semantic variations that exist across different languages, which traditional approaches have often struggled to address effectively. To overcome these challenges, a novel framework XCLHG was developed, integrating a heterogeneous graph structure with multi-view contrastive learning. Specifically, the heterogeneous graph captures syntactic-semantic relationships across languages, while  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Language** & **Dataset** & **Sample** & **Results** \\ \hline \multirow{8}{*}{DE} & \multirow{8}{*}{Amazon Review} & Ein wunderbares Buch. Hab es im Urlaub an einem Tag & \multirow{8}{*}{label: positive} \\  & & durchgelesen und bin begeistert. Genau die richtige Mischung & & \\  & & aus Witz, Phantasie, Trauer und Nachdenklichkeit. Freue mich & & \\  & & schon sehr auf das nachste Buch von [PERSON]! & & \\ \cline{3-4}  & & English translation: A wonderful book. I read it on vacation in & & \\ \cline{3-4}  & & one day and I’m thrilled. Just the right mix of wit, imagination, & & \\ \cline{3-4}  & & grief and thoughtfulness. Looking forward to the next book by & & \\ \hline \hline \end{tabular}  
\begin{tabular}{c c c} \hline \hline RU & XGLUE & \\ \hline \hline \end{tabular}  
\begin{tabular}{c c} \hline \hline \hline \hline \end{tabmulti-view contrastive learning bridges the gap between representations of different languages by maximizing similarity scores for positive samples and minimizing similarity distances between negative samples. The experimental results revealed exceptional performance in CLTC tasks. Evaluated using both F1-score and accuracy, the XCLHG framework consistently outperformed competing approaches. Notably, on the XGLUE dataset, notably, the proposed model achieved up to a 2.20% improvement in accuracy, providing compelling evidence that integrating heterogeneous graph construction with contrastive learning markedly enhances classification precision in CLTC.  
The limitation of XCLHG is that the construction of the heterogeneous graph depends on various language-specific features, such as POS tagging, dependency parsing, similarity metrics, and translation edges. The performance of these linguistic tools may vary significantly across different languages, potentially leading to errors or inconsistencies in graph construction. Future work could address these pressing challenges by fine-tuning or developing domain- and language-specific models for tasks such as POS tagging and dependency parsing, which can reduce errors in the extracted linguistic features and lead to more accurate graph construction. In addition, future work will incorporate a comprehensive time complexity analysis to provide a more complete evaluation of the model's performance and efficiency.  
Methodology, X.L. and K.Z.; software, X.L.; validation, K.Z.; data curation, K.Z.; writing--original draft preparation, X.L.; writing--review and editing, X.L.; visualization, X.L.; supervision, K.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.