[Objective] This paper proposes a scenarized intelligent data-driven research model and conducts preliminary verification through several cases. [Methods] We developed a quantitative characterization model named SDS (S&T decision scenarios(S) - scenarized data alignment(D_X) - solution scenarios(S)). The implementation path of SDS was divided into three steps: S&T decision scenarization demands, scenarized data construction, and optional solution generation. [Results] We verified the model through two cases which supported specific decision-making scenarios such as the selection of emerging and disruptive technologies, the perception of S&T frontier trend, the evaluation of scientific research proposals, and situational awareness in the conflict between Russia and Ukraine. The research results were recognized by relevant S&T decision-makers. [Limitations] The automation level of data scenarized processes is relatively low, and there is a need to improve the combinations of intelligent technologies and information basic theoretical methods in the process of generating evidence chains. [Conclusions] The scenarized intelligent data-driven research model promotes the breadth and depth of research conclusions, improves the efficiency and speed of research work, and verifies the reusability and portability of scenarized intelligent data. It can provide reference and guidance for the concept, ideas, and implementation path of research and services for future S&T decision-making.