Due to the involvement of hazardous materials and the potential serious accidents that may occur in petrochemical plants, it is of great significance to develop real-time state evaluation methods offering high performance. Data-driven methods have received widespread attention following the development of advanced condition-monitoring systems. However, scarce training samples evaluated under multiple operating conditions are available because of the high stability and reliability requirements of petrochemical plants. In this paper, a real-time state evaluation method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which circumvents dependence on data samples. First, the positive and negative ideal solutions of TOPSIS are determined using expert experience and the process index control limits of process cards. Then, fixed-value and fixed-interval indices are proposed to address the interval-optimal parameters. Subsequently, a new combined weight is established using the entropy method and the subjective weight coefficient. Finally, the above steps are integrated into an improved TOPSIS for the state evaluation of petrochemical plants. Experiments conducted on a fluid catalytic cracking (FCC) unit show that the proposed method can quantify the real-time operating status of a petrochemical plant. Furthermore, compared with the equal weight method, the evaluation result of combined weights is more aligned with the actual operating status.