This study constructs a molecular subtype and prognostic model of bladder cancer (BLCA) through endoplasmic reticulum stress (ERS) related genes, thus helping to clinically guide accurate treatment and prognostic assessment. The Bladder Cancer (BLCA) gene expression data was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We clustered by ERS-related genes which obtained through GeneCards database, results in the establishment of a new molecular typing of bladder cancer. Further, we explored the characteristics of each typology in terms of immune microenvironment, mutations, and drug screening. By analyzing the ERS-related genes with univariate Cox, LASSO and multivariate Cox analyses, we also developed the four-gene signature, while validating the prognostic effect of the model in GSE32894 and GSE13507 cohorts. Finally, we evaluated the prognostic value of the clinical data in the high and low ERS score groups and constructed a prognostic score line graph by Nomogram. We constructed four molecular subtypes (C1- C4) of bladder cancer, in which patients with C2 had a poor prognosis and those with C3 had a better prognosis. The C2 had a high degree of TP53 mutation, significant immune cell infiltration and high immune score. In contrast, C3 had a high degree of FGFR3 mutation, insignificant immune cell infiltration, and reduced immune checkpoint expression. After that, we built ERS-related risk signature to calculate ERS score, including ATP2A3, STIM2, VWF and P4HB. In the GSE32894 and GSE13507, the signature also had good predictive value for prognosis. In addition, ERS scores were shown to correlate well with various clinical features. Finally, we correlated the ERS clusters and ERS score. Patients with high ERS score were more likely to have the C2 phenotype, while patients with low ERS score were C3. In summary, we identified four novel molecular subtypes of BLCA by ERS-related genes which could provide some new insights into precision medicine. Prognostic models constructed from ERS-related genes can be used to predict clinical outcomes. Our study contributes to the study of personalized treatment and mechanisms of BLCA.