In order to study the influence of uncertain component failure rates to systematic reliability, a new systematic reliability life-based moment-independent importance measure was presented to analyze the components average impact of component failure rate under uncertainty to systematic reliability life. Inspired by the idea of the Borgonovo moment-independent sensitivity analysis, the proposed method fully takes the complete uncertainty information of systematic reliability life into consideration. Since the moment-independent importance measure was hardly solved accurately due to the implicit format of the inverse function of systematic reliability life function to systematic reliability function, therefore, a new method for Kriging adaptive surrogate model solving was proposed to improve the model prediction precision by adopting the response variation coefficient as the adaptive learning function and automatically increasing new samples. The two test cases of the valve control system and the civil aircraft electro-hydraulic actuator system results show that in the premise of computation accuracy, the Kriging model of systematic reliability life function can be fully approximated by adding small number of systematic reliability life test samples to the variation coefficient adaptive learning function. Hence, the new Kriging model successfully solves the importance measure problem, and the rationality of the proposed method and the high efficiency of the new algorithm are therefore verified.