A viable strategy for Cross-Domain Few-Shot Learning (CD-FSL) involves disentangling features into a domain-irrelevant part and a domain-specific part. The key to this strategy is how to make the model obtain more discriminative features in the target domain to keep accuracy and generalization in the few-shot setting. We propose the Properly Disentangle by Gating and Contrasting (PDGC) framework to accomplish this. It includes a Quaternion Gating Disentangle Module (QGDM) and an Attention-based Spatial Contrasting Module (ASCM). QGDM is utilized to delve deeper into the embedded inter-channel information and mitigate the inherent information loss during the disentangling process. Meanwhile, ASCM is utilized as a regularization constraint to avoid over-focusing on seen classes on CD-FSL problems leading to excessive disentangling and loss of generalization ability. Compared to the baseline, our method obtains an average of 2.3% and 3.68% improvement in 5-way 1-shot and 5-way 5-shot respectively in the FWT’s benchmark, and improves on most of the datasets in the BSCD-FSL’s benchmark.