Aim An extracellular matrix (ECM) - related gene prognostic model was constructed to evaluate its ability to predict the prognosis of glioma patients and explore the glioma immune microenvironment characteristics based on this model. Methods Based on glioma as well as normal brain tissue data from the tumor Genome Atlas (TCGA) and genotype tissue expression (GTEX) databases, differential expressed genes (DEGs) were filtered. ECM-related genes were obtained based on the Gene Ontology（GO） database, and glioma prognostic genes were obtained based on cox proportional-hazards model. Overlapping candidate genes were obtained by intersection of the above three parts, and then the best 4-gene prognostic model was obtained by lasso analysis, followed by survival analysis and Cox regression analysis in the glioma cohort in TCGA as well as the Chinese glioma Genome Atlas (CGGA) database. A prognostic nomogram was constructed based on the 4-gene prognostic model as well as TCGA patient prognostic data and validated in the CGGA glioma cohort. Finally, the immune microenvironment features associated with the 4-gene prognostic model were explored based on enrichment analysis, immune checkpoint analysis as well as immune infiltration analysis. Results The 22 overlapping candidate genes were subjected to lasso analysis to obtain the best 4-gene prognostic model, and the risk score of this model was able to better predict the prognosis of TCGA as well as CGGA glioma patients, and was an independent risk factor for poor patient outcome. Cell line validation experiments suggested that the U251 cell line (glioma cells) had a higher expression of all optimal 4 genes than the HMC3 cell line (microglia), which is consistent with the results of the TCGA as well as the CGGA database analysis. The prognostic Nomogram constructed based on the 4-gene prognostic model similarly had better ability to predict patient prognosis. Patients in the high-risk group had higher levels of M2 macrophage infiltration and significantly higher levels of immune checkpoint related molecules (PD-L1, B7-H3, CTLA4, PD1, tim3 as well as Lag3) in tumor tissues than those in the low-risk group. Conclusion Both the ECM related gene model as well as the prognostic Nomogram were able to better predict the prognosis of glioma patients, and patients in the high-risk group had a suppressive immune microenvironment characteristic, and immune checkpoint inhibitors may be a potential treatment modality for such patients.