To proactively assess the losses caused by the deterioration of metro tunnels during the operational period, a new method, the cloud model‐based random forests (CRFs), is proposed to discuss the inconsistencies induced by mapping the monitoring data into the health rate of the metro tunnels. On top of the CRF, a self‐training framework is introduced to improve the predictive accuracy and the stability of the CRF by adding more unlabeled data. The main contribution of this paper is proposing a novel CRF model along with a semisupervised approach to overcome the inapplicability of the random forests algorithm in an inconsistent small database, and to reduce the substantial time and cost for expert annotation. The results indicate that (1) the proposed CRF achieves higher accuracy and stability than random forests in predictions; (2) the CRF outperforms the other state‐of‐the‐art methods even in a small database; (3) the self‐training improved CRF keeps highly precise when the ratio of labeled to unlabeled data is no less than 1:11.4. In this study, the suggested ratio of labeled and unlabeled data is no lower than 1:5.7 to reduce the risk of wrongly forecasting a seriously damaged tunnel section as a slightly damaged tunnel section.