The work aims to explore how generative AI models align with designers' intentions in human-AI co-creation from the perspective of artificial intelligence alignment. Methodologically, the interpretability and controllability issues in AI alignment were explored through a feature analysis approach. The research examined how generative AI technologies, as auxiliary design tools, align with the specific intents and needs of the three stages of the design process:"exploration, innovation, and evaluation". The alignment challenges that needed to be addressed in each stage were analyzed. Technologically, the study proposed an interactive alignment method, representation expansion method and representation evaluation method based on human intelligence design concept representation. In conclusion, the study constructs three alignment methods:intent alignment, design space expansion, and design rule matching, in the three stages. These methods aim to assist designers in building controllable and interpretable human-AI co-creation methods to contribute to controllable and trustworthy human-AI co-creation.