Knowledge graph (KG) alignment aims to integrate different KGs through the linkage of equivalent entities across them, enabling more comprehensive knowledge and facilitating information fusion. Existing methods, whether translation-based or GNN-based, typically solve this problem by projecting entities and relations into a low-dimensional embedding space, each demonstrating unique advantages in aligning a pair of KGs. However, few studies consider combining these approaches to model translation semantics of various orders. To fill this gap, we propose KG2HIN, a novel KG encoder, which innovatively views head entities, relations, and tail entities as three types of nodes, thereby transforming KGs into HINs (heterogeneous information networks). KG2HIN can adaptively learn the importance of various orders of translation semantics by seamlessly combining the HGNN aggregator operator with the translation operator in KG embedding methods. Building upon the KG2HIN encoder, we further develop a network to effectively and efficiently align multiple (more than two) KGs concurrently, a much more challenging task than the traditional pair-KG alignment task. Compared with the state-of-the-art baseline, KG2HIN significantly improves the M-Hits@1 (accuracy) score from 10.25% to 73.05% on the DBP4 dataset and from 41.19% to 97.81% on the DWY-3 dataset, while requiring significantly fewer model parameters and less training time.