Cross-lingual entity alignment is an important step in knowledge fusion, and its purpose is to determine whether two or more entities from different information sources refer to the same object in the real world. Recently, with the development of representation learning, many neural network-based entity alignment methods have been proposed. Existing embedding-based entity alignment methods embed entities from different information sources into a unified space, and then calculate the similarity between entity vector representations to find similar entities. Although the existing neural network-based methods have shown good performance on some public datasets, they also inherit some problems of neural networks, such as inexplicability and high model complexity, and most models are not suitable for the structural isomorphism of similar entity pairs is relatively high, which greatly limits the scalability of the model. Based on this, we propose our model on how to reduce the requirement for isomorphism—Multi-Depth Joint Entity Alignment Based on Text Information (MDJEA), which utilizes the name information and attribute information of entities, and simultaneously transforms the entity alignment problem into the allocation problem, which greatly improves the interpretability and robustness of the model, and can achieve alignment without training data. At the same time, we also combine the multi-depth information of the entity and combine the structural information at different depths to improve the alignment result. Experiments show that our method outperforms existing models by 2.7%-3.5% on each dataset.