Aiming at the difficulty of static weight allocation in electronic Internet of Things(EIoT) security situation awareness, which is difficult to accurately reflect the actual network security situation, resulting in limited perception accuracy, this paper proposes an improved Bayes network based EIoT security situation awareness method. It selects and categorizes key situational indicators based on the characteristics and security requirements of EIoT. In view of the complexity and variability of the security situation assessment of EIoT, an improved Bayesian network model is constructed, and the attribute weighting algorithm and judgment factor are introduced to improve the classification accuracy and robustness of the model, and effectively cope with the difference and importance of different situation indicators. On this basis, by integrating the underlying situation indicators layer by layer and quantifying significant influencing factors, the security situation level of EIoT is finally determined, and the accurate perception and evaluation of the security situation of EIoT is realized. The experimental results show that the perceived situation value of the research method is highly consistent with the actual situation value, which can significantly improve the accuracy of the security situation awareness of EIoT.