Background: Oral squamous cell carcinoma (OSCC) is recognized as one of the top ten malignancies worldwide, and is usually characterized with poor prognosis and low rates of overall survival. Consequently, the risk evaluation of prognosis and recurrence is critical for the clinical decision and outcome. Recently, these OSCC-associated lncRNAs were suggested to be used as potential prognostic biomarkers and monitoring tools. This study aimed to evaluate the potential utility of lncRNAs in constructing lncRNA-based classifiers of OSCC prognosis and recurrence. Methods and results: Based on the data concerning OSCC downloaded from TCGA, lncRNA-based classifiers for OS and RFS were built using the least absolute shrinkage and selection operation (LASSO) Cox regression model in the training cohorts. Furthermore, a 36-lncRNA-based classifier for OS and a 16-lncRNA-based classifier for RFS were constructed by means of the LASSO Cox regression model. According to the prediction value, patients were divided into high/low-risk groups and the log-rank test showed significant differences in OS and RFS between high- and low-risk groups in three cohorts. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cut-off point was obtained from ROC analysis. The risk score could successfully differentiate the patients into high- and low-risk groups, and significant differences were established in OS and RFS between low- and high-risk groups in all the three cohorts. The survival and relapse-free time of the high-risk group was significantly lower than that of the low-risk group. In addition, the combination of the lncRNA-based classifier models and TNM staging could slightly enhance the ability to predict prognosis of survival and recurrence. Conclusions: The superiority of this signature in OSCC prognosis prediction was proved in this study. In conclusion, these classifiers could represent promising biomarkers fo