Cross-lingual sentiment classification is very important in natural language processing and has been widely studied. It uses label information from the source language to construct a sentiment classification system for the target language, thereby greatly reducing the laborious labeling work in the target language. A fundamental challenge in cross-lingual sentiment classification is the obvious difference in the expressions of different languages. This study proposes a method for cross-lingual sentiment classification based on a bilingual dependency graph model. Although the expressions in different languages are various, their internal syntactic dependencies are similar. By establishing edges among word nodes in different languages to represent the semantic relevance of bilingual comment instances, the bilingual dependency graph can explicitly model the similarity of the dependency relationships among words in different languages, allowing graph neural networks to integrate syntactic structure information within and across languages for cross-lingual sentiment classification. Experiments conducted on datasets in both English and Chinese show that the proposed method achieves an improvement of 3% over the baseline method. It is proven that bilingual dependency graphs can effectively model the correlation of comment instances in different languages, thereby significantly improving the accuracy of cross-lingual sentiment classification.