Aspect-based sentiment analysis has garnered increasing attention in the research community; however, most studies have predominantly focused on English datasets, with other languages such as Chinese, Japanese, and German being neglected due to the limited availability of adequately labeled data. Even within English, labeled data is scarce. To address these challenges, this study investigates the utilization of a multilingual pre-trained setting to leverage resources from diverse languages for aspect-based sentiment analysis. Specifically, we propose a Cross-lingual Knowledge Fusion framework that explores various single-round and two-round bilingual pre-training configurations. This framework utilizes both the original and translated texts, along with their corresponding labels, to pre-train the multilingual model. Evaluation results reveal that our model significantly outperforms state-of-the-art performance across multiple languages, highlighting the effectiveness of the proposed multilingual pre-trained language model for aspect-based sentiment analysis.