Objective To develop a radiomics nomogram to predict the recurrence of Low grade glioma(LGG) after their first surgery; Methods A retrospective analysis of pathological, clinical and Magnetic resonance image(MRI) of LGG patients who underwent surgery and had a recurrence between 2017 and 2020 in our hospital was performed. After a rigorous selection,64 patients were eligible and enrolled in the study(22 cases were with recurrent gliomas),which was randomly assigned in a 7:3 ratio to either the training set and validation set; T1WI,T2WI fluid-attenuated-inversion-recovery(T2WI-FLAIR) and contrast-enhanced T1-weighted(T1CE) sequences, 396 radiomics features were extracted from each image sequence, minimum-redundancy maximum-relevancy(mRMR) alone or combining with univariate logistic analysis were used for features screening, the screened features were performed by multivariate logistic regression analysis and developed a predictive model both in training set and validation set; Receiver operating characteristic(ROC) curve, calibration curve, and decision curve analysis(DCA) were used to assess the performance of each model. Results The radiomics nomogram derived from three MRI sequence yielded an ideal performance than the individual ones, the AUC in the training set and validation set were 0.966 and 0.93 respectively, 95% confidence interval(95%CI) were 0.949-0.99 and 0.905-0.973 respectively; the calibration curves indicated good agreement between the predictive and the actual probability. The DCA demonstrated that a combination of three sequences had more favorable clinical predictive value than single sequence imaging. Conclusion Our multiparametric radiomics nomogram could be an efficient and accurate tool for predicting the recurrence of LGG after its first resection.