In this paper, relevant cultural materials were first collected and extracted using knowledge mapping and retrieval tools, and the co-occurrence analysis method was used to construct the co-occurrence relationship matrix between keywords. Combined with the method of graphic vectorization, the traditional cultural and creative product design patterns and motifs are directly scanned to extract the design prototype’s outer contour and polygonal path. Next, the recommendation process of cultural and creative products was realized by combining collaborative filtering and the TransD-CF algorithm. Finally, the influence of cognitive attitude and purchase behavior indicators on cultural and creative products was analyzed through correlation. The results show that the difference between genders using essay on hobby collection of cultural and creative products is 0.203, and the practicality requirement of cultural and creative products decreases by 0.09 for every 100 yuan increase in price. This paper presents a fresh perspective on cultural and creative promotion and plays a guiding role in design practice with the aid of artificial intelligence.