Abstract: The technology roadmap holds significant strategic importance for the development of new quality productive forces in China. However, the existing methods for constructing technology roadmap still need to make trade-offs between ease of use and accuracy, and the intelligence level also needs to be improved. Moreover, the accuracy of mining information in technology roadmap based on network data source still needs to be optimized. As artificial intelligence enters a new stage, how to simultaneously enhance the efficiency, accuracy, and intelligence level of technology roadmap construction method based on AI technology is an urgent issue to be solved today. Therefore, given the existing limitations in current research, this study proposes an AI-enhanced framework for technology roadmap construction. Firstly, conduct theme evolution analysis for the three dimensions of "technology" - "product function" - "market" respectively. In the technology dimension, based on the evaluation index system of frontier technologies, the data set is subject to theme clustering through the screening of patents and academic papers, and the theme evolution of the technology dimension is analyzed in combination with the time dimension. In the product function dimension, the text classification of Large Language Model(LLM) is used to screen product news related to the field, and based on this, information extraction of product categories and functions is carried out, and the product theme evolution is analyzed in combination with the time dimension. In the market dimension, for two types of data sources, namely news, scientific and technological reports, the methods of "generative summary by LLM + clustering" and "TRT semantic analysis" are respectively adopted, and the market theme evolution is analyzed in combination with the time dimension. Secondly, the themes among various dimensions are associated through keywords matching. The technical points and market applications in the product function sentences are extracted based on LLM, and are respectively matched with the theme words of the technology dimension and the market dimension to establish the associations among various dimensions. Finally, based on the Retrieval-Augmented Generation (RAG) technology and the interactive dialogue with LLM, the framework implements systematic prediction of technological development.Utilizing the knowledge database built with futuristic data, through the multi-round interaction with the RAG-enhanced LLM, the future application scenarios are comprehensively predicted, and based on these scenarios, the technological development trends in the future are scientifically forecasted, completing the entire technology roadmap construction process. An empirical analysis is conducted in the field of UAV target identification and tracking, constructing a technology roadmap for this field. Three realized technical routes are identified, including radar detection, computer vision, and laser detection. The analysis reveals three characteristics of future technological development in the field of UAV target identification and tracking, namely "systematization," "intelligence," and "modularity". This study provides strong support for the development of the UAV target identification and tracking field and validates the scientificity and feasibility of the proposed framework, which makes the drawing of the technology roadmap more efficient, accurate, and convenient, and enhances the intelligence level of the node construction and prediction process of the technology roadmap.