## 1 Introduction  
Deep learning [1], a prominent subfield of artificial intelligence, leverages neural networks to identify complex patterns in large datasets, driving significant progress across multiple fields [2, 3]. In particular, it has made substantial strides in numerous Natural Language Processing (NLP) tasks, such as text classification [4], machine translation [5], and information retrieval [6]. However, a significant limitation of deep-learning models is their heavy reliance on large amounts of labeled data for optimal performance [7]. Labeled data, however, is often scarce for low-resource languages. As a result, an increasing number of recent studies are focusing on the field of cross-lingual knowledge transfer [8, 9]. Cross-Lingual Text Classification (CLTC) [10, 11, 12] is a branch of cross-lingual knowledge transfer task, aiming to classify texts in target languages with limited or no annotated data by training a classifier on source language data. The main challenge in CLTC is overcoming the inherent semantic and syntactic disparities between languages, with the ultimate goal of enabling effective transfer knowledge from the source language to the target language. Existing methods mainly implement the semantic similarity among languages to bridge cross-lingual gaps [13], including effective multilingual pretrained language models [9; 14], which leverage transformer-based neural networks trained on large-scale multilingual datasets. However, the performance of multilingual pretrained language models are often constrained by their ability to capture syntactic information and handle complex structural dependencies across languages [15]. Furthermore, their failure to explicitly model interlingual syntactic variations may ultimately compromise generalization performance in the target language. To overcome the limitations in learning representations of textual syntactic structures, recent researchers have focused on leveraging Graph Neural Networks (GNNs). These models capture relational information inherent in graphs by aggregating and transforming information from neighboring nodes [16], thereby enabling them to model both local and global dependencies and effectively capture global long-range word dependencies and syntactic structures. Motivated by recent breakthroughs in GNN-based CLTC and driven by the need to address existing challenges [17], [PERSON] et al. [18] introduced a heterogeneous graph construction method to encode both source and target texts. However, this model neglects the dependency relationships between words, and the use of machine translation often introduces substantial inaccuracies, compromising the reliability of results. On this basis, this study proposes a heterogeneous GNN framework integrated with multi-view contrastive learning, named XCLHG, to effectively capture structured information by combining semantic and syntactic knowledge. In this framework, a heterogeneous graph is constructed in which words and documents serve as nodes, connected by various types of edges, including Part-of-Speech (POS) tagging, dependency, translation, and similarity edges. Additionally, in order to improve the quality of language representations by bringing semantically equivalent samples from different languages closer together in the embedding space, this study devises a multi-view contrastive learning strategy; the two views are Translation-Level Contrastive Learning (TL-CL) and Label-Level Contrastive Learning (LL-CL), respectively. This study presents a novel framework, XCLHG, that integrates a heterogeneous GNN with multi-view contrastive learning to address the challenges of CLTC. The main contributions are as follows:  
1. Theoretical advances: The proposed framework introduces an innovative approach that captures both syntactic and semantic knowledge by constructing a heterogeneous graph, where documents and words are interconnected through multiple edge types.