The prognosis of patients with recurrent low-grade glioma (rLGG) varies greatly. Some patients can survive more than 10 years after recurrence, while other patients have less than 1 year of survival. In order to identify the related risk factors affecting the prognosis of rLGG patients, we performed a series of bioinformatics analyses on RNA-sequencing data of rLGG based on the CGGA database, and finally constructed a 12-genes prognostic signature, dividing all the rLGG patients into high- and low-risk subgroups. The result showed an excellent predictive effect in both the training cohort and the validation cohort using LASSO-COX regression. Moreover, multivariate COX analysis identified 4 independent prognostic factors of rLGG, and among them, ZCWPW1 is identified as a high-value protective factor. In all, this prognostic model displayed robust predictive capability for the overall survival (OS) of rLGG patients, providing a new monitoring method for rLGG, and the 4 independent prognostic factors, especially ZCWPW1, can be potential targets for rLGG, bringing new possibilities for the treatment of rLGG patients.