Objective: As the central role of innovation elements in economic and social development continues to rise, based on the practical need of the country to "strengthen the evaluation of the relationship between patent activities and economic benefits", breaking through the barriers between patent databases and other economic databases is a substantial development in the economic and national analysis research fields. The establishment of connections and data networks across various databases, such as product and patent databases in different fields and systems, is required to explore the correlation, internal mechanisms, and heterogeneity of innovative applications and transformations. Existing studies have mainly focused on the mapping between patents and industries. However, the complexity of product classification has led to the absence of direct mapping between International Patent Classification (IPC) and Harmonized System (HS) code, which has limited the analysis of technology transfer and industry-technology adaptation mechanisms. This paper aims to construct a cross-database technology-product category mapping method, reveal the technological characteristics of segmented industries, and provide data support for industrial innovation research. Methods: This paper utilizes the classification information of patent and product databases to explore the full-category mapping relationship between patent IPC classification and product HS classification in the Chinese language environment. Based on the comprehensive method of natural language processing (NLP), cross-searching, and algorithmic links with probabilities (ALP), this paper employs the examples of products corresponding to the HS codes from the data released by the General Administration of Customs of China as external word sources to expand the HS category keywords, thereby obtaining a keyword list with higher quality than that generated by NLP segmentation. Furthermore, three weighting correction methods(raw weight, specificity weight, and hybrid weight) are employed based on the Bayesian theorem to establish mapping links between HS (six-digit) and IPC (three-digit); these are combined with multilevel classification to refine the analysis of technological differences and associations. Results: The mapping results reveal that complex products are associated with a wide variety of technologies, whereas simple industrial and agricultural products are associated with fewer technology types. The results reflect the heterogeneity of technological innovation across different industries and products. The calculation results of specificity and mixed weights are more likely to reveal unique technology types related to the production of certain product categories compared with the original weight, which is of great importance for further identifying specialized, sophisticated, and novel patents. The development of strategic emerging industries is closely related to the technological support of sections G (Physics) and H (Electricity), objectively indicating the importance of basic research in the development of strategic emerging industries. Conclusions: The IPC-HS link method constructed using cross-searching and ALP can effectively quantify the strength of technology-product associations, break through the barriers of the classification systems between technology and products from the perspective of innovation achievement transformation, and provide data-driven empirical support for the transformation of technological achievements. This mapping relationship can reveal the technological characteristics and differences of segmented industries; it can contribute to the understanding of technology diffusion in the innovation ecosystem, the application of technology in strategic emerging industries, and the adaptation mechanism between technology and industry.