This paper conducted a thorough investigation into the primary difficulty of the cross-channel automatic modulation classification (AMC) task by examining data distribution and feature space of different channel conditions. We concluded that the disruption of the target channel feature space structure breakdown the mapping relationship across channels, serving as the main contributor to model performance degradation. Based on the above conclusion, in order to improve the performance of cross-channel AMC, we introduce the Auxiliary Feature Learning-Guided Network (AFLNet). This network improves the structure of the target feature space through two uniquely designed tasks and facilitates efficient cross-domain alignment via a collaborative alignment mechanism. Specifically, AFLNet integrates similarity-based and confidence-based auxiliary feature learning tasks to enhance the discriminability of the target feature space and maintain the correspondence of category structures across different channels, thereby reducing the difficulty of feature alignment. The collaborative alignment mechanism combines adversarial training-based and self-training-based feature alignment methods, leveraging their mutually reinforcing effect and complementary strengths in global alignment and class-level alignment to enhance overall alignment performance. We carried out extensive experiments across four scenarios characterized by substantial channel variations, verifying that AFLNet achieves state-of-the-art with accuracy improvement of up to 9.71%.