This study aimed to construct a long noncoding RNA (lncRNA)-based prognostic signature to improve the survival prediction for endometrial cancer (EC) patients and guide individualized treatments. mRNA and miRNA sequencing and clinical data of 526 patients with EC (randomized to training or validation set, n = 263) were collected from The Cancer Genome Atlas database. Differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) were identified between 263 EC samples and 33 normal controls. Univariate and multivariate Cox regression analyses identified five DELs (LINC00475, LINC01352, MIR503HG, KCNMB2-AS1, and LINC01143) that were overall survival related. The Kaplan-Meier curve showed that the risk score model established by these five DELs can significantly distinguish the survival ratio of patients at high risk from those at low risk. The receiver operating characteristic curve indicated that this risk score exhibited good survival prediction performance, with the area under the curve of 0.978. In addition, this risk score was independent of other clinical factors. Stratification analysis based on two independent prognostic clinical factors (histologic grade and recurrence status) demonstrated that the high-risk score was still a poor prognostic factor for patients with histologic grade 3, recurrence or nonrecurrence status. In nomogram model, the risk score was one of the main contributions to survival rates, and its Harrell's concordance index was higher than the other two independent clinical factors, although all lower than the combined. Furthermore, mechanism analyses showed that these lncRNAs functioned by coexpressing with DEGs (i.e., LINC00475-PTGDR, LINC01352/MIR503HG-BACH2, KCNMB2-AS1-PCSK9, LINC01143-NUF2/PTTG1) or as a competing endogenous RNA of DEMs to regulate DEGs (LINC00475-miR-4728-PTGDR, MIR503HG-miR-3170-BACH2). In conclusion, our novel risk score system may be a promising prognostic biomarker to guide personalized treatment for EC patients and it can add prognostic value for current clinical system.