With the rapid advancement of globalization, the increasingly complex global value chain (GVC) division system has profoundly reshaped production modes and factor allocation patterns worldwide, driving the development of international circulation. The international production network, formed through interconnected cross-border industrial supply and production chains, serves as a critical carrier of this circulation, facilitating the diffusion of technologies and knowledge. However, rising global uncertainties—particularly frequent “black swan” events over the past 15 years—pose external risks that may weaken the technology spillover effects of international production networks, thereby diminishing their economic positive externalities. Current research remains nascent in algorithm-based quantification of technology spillover effects within production networks, with limited attention to how risk shocks might attenuate these effects. This study first constructs a multi-sectoral production network general equilibrium model incorporating risk shocks, disentangling and measuring international circulation-driven technology spillover effects. Leveraging intermediate goods input-output data from 35 industries across 60 countries (2008—2022) in the Asian Development Bank (ADB) database, this study empirically examines how international production network correlation degree influences industry output growth and whether technology spillover effects mediate this relationship. Further, it analyzes whether risk shocks weaken the output-enhancing effects of international production networks. Results indicate that network correlation degree boosts industry output growth via technology spillover effects, yet this growth exhibits heterogeneity across industries, countries, and centrality levels. Risk shocks significantly dampen technology spillover effects, thereby reducing the growth-promoting role of network correlation degree, with heterogeneous impacts contingent on sectoral and national contexts. Compared to existing literature, this study contributes three key advancements: First, it bridges the theoretical divide between technology spillovers and risk shocks by endogenizing risk shocks within the technology spillover transmission mechanism, enabling coupled analysis of positive technological and negative risk effects. Second, departing from conventional “micro-to-macro” aggregation approaches, it pioneers a reverse analytical framework linking “macro-network structures to micro-sector performance”. Third, it systematically employs the ADB multi-country input-output database to quantify technology spillover effects as a mechanism variable, providing novel empirical evidence on how international production network correlation degree shapes industry output growth. This research deepens understanding of real economic downturns and their drivers, offering insights for China’s dual-circulation development paradigm and strategies to mitigate systemic risks.