Abstract Due to limitation of labeled resources, crossdomain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing crossdomain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from crossdomain LM contrast, allowing unsupervised domain adaptation while also giving state-ofthe-art results among supervised domain adaptation methods.