The intelligent transformation of the apparel design industry needs to simultaneously meet the requirements of both efficiency improvement and personalization promotion. This paper proposes an intelligent design framework that integrates curve theory, garment prototyping and meta-learning technology. It optimizes the design of apparel by using the smoothness constraints of interpolation curves, the flexibility expression of parametric curves, and the local optimization characteristics of Bspline curves. Combine the prototype-based thinking model with meta-learning method to solve the generalization problem under small sample data and improve the model adaptation speed. The practical efficiency enhancement level and application value of the methods in this paper are verified through practice and testing, etc. The results show that the parametric design can realize the fast garment styling change of single parameter and multi-parameter. The optimization algorithm combining prototyping and meta-learning always takes less than 20 seconds in the 10-parameter range adjustment experiments, which is faster than the comparison algorithm. In the comprehensive fuzzy evaluation of experts and consumers, “very satisfied” and “good” account for 63.99% and 58.42%, respectively. The method based on technology fusion in this paper can significantly improve the design efficiency and user satisfaction of clothing personalization.