Research on Risk Measurement Methods of Scientific and Technological Innovation: A Dynamic Tension Model Based on Novelty and Adaptation

Grounded in knowledge recombination theory and innovation tension theory, this study develops a novel measurement framework for scientific and technological innovation (STI) risks that captures the dynamic and systemic equilibrium between novelty and adaptation. We first analyze the endogenous mechanisms through which STI risks emerge from knowledge recombination processes, and then propose a classification framework for knowledge recombination, along with quantifiable metrics for novelty and adaptation. Next, we introduce a risk classification system for STI and corresponding quantitative evaluation metrics, facilitating dynamic monitoring of innovation risk states. Finally, we validate the framework through an empirical case study in natural language processing (NLP). Our results reveal a persistent innovation tension within the STI system between novelty and adaptation. Emerging phrases and reinforced phrases demonstrate distinct risk profiles and distribution patterns, corresponding to differentiated structural and evolutionary regimes. These differences stem from their distinct mechanisms in the novelty–adaptation interaction within a complex innovation system. Specifically, in emerging phrases, novelty shows a stable positive linear correlation with Z-score, while adaptation exhibits a significant negative linear correlation with Z-score. In reinforced phrases, novelty displays a significant bimodal association with Z-score, and adaptation demonstrates a robust inverted U-shaped relationship with Z-score. Emerging knowledge combinations show significantly higher risk scores than reinforced combinations, with high-novelty–low-adaptation combinations consistently in the highest risk quantile across stages. Moreover, the risk threshold for emerging phrases increases monotonically across developmental phases. Thus, our framework advances innovation risk assessment from static categorization to dynamic, system-level evaluation, enabling tiered risk management and optimized resource allocation for high-potential innovation pathways.