Background: Adenocarcinomas of the lung (LUAD) constitute the most common type of non-small cell lung cancer (NSCLC), which is experiencing the fastest rate of growth. Unlike apoptosis, necroptosis is a new type of programmed cell death that plays a critical role in cancer biology. In this study, a necroptosis-related prognostic model was examined in relation to LUAD prognosis. Method: We downloaded lung adenocarcinoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Necroptosis-related genes were compiled from Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Differentially expressed necroptosis-related genes and the prognostic value of necroptosis-related genes were identified using the “Limma” package in R and univariable Cox regression analysis. the least absolute shrinkage and selection operator (LASSO) COX were performed to screen the the necroptosis-related genes (NRGs) and establish the prognostic prediction model. To explore potential functions and pathways, functional enrichment, ESTIMATE algorithm, and single-sample Gene Set Enrichment Analysis (ssGSEA) were applied. We also explored the relation between risk score and immune checkpoint. The data downloaded from Gene Expression Omnibus (GEO), the Human Protein Atlas and Timer database were used for external validation. Result: Based on 8 genes related to necroptosis, we constructed a prognostic model. The data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) was divided into high-risk group and low-risk group, respectively. The high-risk group had a significantly worse prognosis than the low-risk group. An independent factor influencing OS was the risk score. Nomograms were constructed by risk score and clinical data. These NRGs were mainly related to adaptive immune response regulation, according to the result of functional analysis. Conclusion: In conclusion, the eight-gene prognostic model could be used to predict the prognosis of patients with lung adenocarcinoma (LUAD). We also explored the relationship between this model and tumor microenvironment.