## 2 Related Work
### Pre-Trained Methods  
Pre-trained models transfer knowledge during the pre-training phase and perform classification tasks during the fine-tuning phase [26]. Based on the central content of this study, pre-trained models are subdivided into three categories: transformer-based models, GNN models, and contrastive learning models. Transformer-based models offer significant advantages over traditional linear mapping methods, primarily in the following aspects [12]. They apply neural network architectures to capture the dynamic semantics of words and sentences, which enhances contextual awareness. Furthermore, they employ a shared multilingual vocabulary and joint pre-training to facilitate implicit semantic space alignment. [PERSON] et al. [27] proposed a meta-learning-based framework to push the source and target language representations closer together. [PERSON] et al. [28] integrated topic modeling with a graph attention network to enhance the semantic information of textual representations in multilingual context. [PERSON] et al. [29] categorized languages into three groups based on central languages and applied meta-learning to optimize parameter initialization. [PERSON] et al. [26] incorporated a code-switching technique and a syntactic-based mBERT model to capture syntactic and lexical information for cross-lingual knowledge transfer. [PERSON] et al. [30] proposed a framework that preprocesses target language data by translating it into the base language, and classification results were determined by a majority vote from three distinct models. Typically, transformer-based methods outperform those relying on linear transformations. However, these approaches often overlook the structural information inherent in sentences, which is crucial for accurate text classification [17]. Compared to transformer-based approaches, GNNs offer distinct advantages for cross-lingual text classification by capturing the complex relational structures inherent in text [18]. GNNs excel at modeling the intricate syntactic and semantic dependencies critical to understanding multilingual data. Moreover, they naturally facilitate multi-scale representation learning by aggregating information from local neighborhoods, capturing fine-grained, word-level co-occurrences and syntactic patterns, as well as those from broader global contexts that reflect document-level semantic coherence [31]. In the field of text mining, numerous studies employing GNNs, such as TensorFlowGCN [32] and TG Transformer [33], have consistently surpassed transformer-based methods in performance. Nonetheless, these GNN-based techniques have yet to fully capture the global semantic information embedded in text documents, nor have they provided sufficiently structured representations. Recent studies have focused on modeling long-term heterogeneous dependencies among documents to enhance text representation quality [12]. Extensive experiments in these studies have demonstrated that heterogeneous text graph modeling and representation learning, realized through various GNN architectures, can yield more robust and informative representations [18]. However, a notable limitation of GNNs is that they assign equal weight to all neighboring nodes, which does not reflect real-world scenarios. To solve this issue, a Graph Attention Network (GAT) was introduced to adjust and assign different weights to neighbors using a self-attention mechanism [34]. GATs offer inherent flexibility in capturing complex, node-dependent relationships within graph-structured data, effectively addressing the key limitations of traditional GNNs, such as the over-smoothing phenomenon [35]. With its novel attention mechanism, GATs consistently outperform conventional methods across a wide range of graph-based tasks. Building on these strengths, [PERSON] et al. [18] introduced a heterogeneous GAT to encode both source and target texts. The fundamental principle of contrastive learning lies in constructing sample pairs to learn more discriminative feature representations.