Cross-domain named entity recognition aims to alleviate the problem of insufficient annotation data in the target domain. Most existing methods, which exploit the feature representation or model parameter sharing to achieve cross-domain transfer of entity recognition capabilities and can only partially utilize structured knowledge entailed in text sequences. To address this, we propose a multi-level structured semantic knowledge enhanced cross-domain model (MSKE-CDNER), which could facilitate the transfer of entity recognition capabilities by aligning the structured knowledge representations embedded in the source and target domains from multiple levels. First, the MSKE-CDNER uses the structural feature representation layer to achieve structured semantic knowledge representations of texts from different fields by structured alignment. And then, these structured semantic representations are aligned at the corresponding layers by a latent alignment module to obtain cross-domain invariant knowledge. Finally, this cross-domain consistent, structured knowledge is fused with domain-specific knowledge to enhance the model's generalization capability. Experiments on five datasets and a specific cross-domain named entity recognition dataset have shown that the average performance of MSKE-CDNER improved by 0.43% and 1.47% compared with the current models. All of these indicate that exploiting text sequences' structured semantic knowledge representation could effectively enhance entity recognition in the target domain.