<p>Currently, frequent geopolitical conflicts have exacerbated the tail risk contagion phenomenon in the international crude oil market, which constitutes a serious challenge to global energy security. This study utilizes the complex network approach to construct an international crude oil market tail risk network to examine the tail risk contagion characteristics. Furthermore, based on machine learning and macro risk indicators, early warning of international crude oil volatility risks was provided. It is found that the overall risk spillover pattern of the international crude oil market is significantly and strongly impacted by extreme events such as geopolitical conflicts and financial crises. Countries in the Middle East region dominate the risk spillover pattern in the international crude oil market. In addition, the tail risk contagion effect in the international crude oil market shows a long-term risk dominant pattern. In contrast, short-term risk contagion is more susceptible to external factors and exhibits higher volatility. The results of crude oil price volatility early warning show that the early warning system constructed by nonlinear CoVaR, high-frequency and low-frequency international crude oil market tail risk contagion indicators and macro risk indicators can effectively warn of international crude oil price volatility. The conclusions of the study are significant for preventing tail risk contagion among international crude oil markets and maintaining the stability of the global energy market.</p>