Most of the mature labeled dataset of aspect-level sentiment analysis are in English, it is quite rare in some low-resource language such as Chinese. For the sake of utilizing the vast but unlabeled Chinese aspect-level sentiment classification dataset, this study works on cross-lingual aspect-level sentiment classification. Nevertheless, the most central and difficult problem in cross-lingual mission is how to construct the connection between the documents in two languages. In order to solve this problem, this study proposes a method using graph neural network structure to model the connection of multilingual word-to-document and word-to-word, which could effectively model the interaction between the high-resource language (source language) and low-resource language (target language). The connections include multilingual word-to-document connection and monolingual word-to-document connection are constructed to tie the source language data and target language data, which are modeled by graph neural network to realize using English labeled dataset as trainset to predict Chinese dataset. Compared with other baseline model, the proposed model achieves a higher performance in F1-score, which indicates that the presented work does contributing to the cross-lingual aspect-level sentiment classification.