Lung adenocarcinoma (LUAD) is the most widely known histological subtype of lung cancer. Its classification is significant for the characteristic evaluation of patients. The aim of this research is to assess the categorization of LUAD and its risk model based on necroptosis and to investigate its potential regulatory mechanisms for diagnosing and treating LUAD. According to the expression profile data along with the clinical information related to LUAD from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we constructed a consistency matrix through consistency clustering, and used the ConsensusClusterPlus as the measurement distance to cluster and subtype the samples, and performed gene set enrichment analysis and immune infiltration analysis. Least absolute shrinkage and selection operator (Lasso) regression was utilized for obtaining prognostic significant necroptosis phenotype-related genes. Finally, we measured each patient's riskscore (RS) and build a risk model, and predicted the effect of immunotherapy for different groups of risk factors in the model. Three molecular subtypes of LUAD were obtained by cluster analysis of necroptosis-related genes in LUAD samples. Compared with C1, C3 had a better prognosis and higher immune cell infiltration. The prognosis of the C1 subtype was poor and had a high clinical grade. The proportion of Stage II, Stage III, and Stage IV was much more in comparison with that of the other two subtypes. TP53 gene had a high mutation frequency in the C1 subtype. Gene Set Enrichment Analysis (GSEA) indicated that the aberrant pathways in the C1 and C3 subtypes mainly included some cell cycle-related pathways. In addition, seven genes were identified as related genes of necroptosis phenotype affecting prognosis. High RS had a poor prognosis, while low RS had a good prognosis. The RS was verified to have a strong ability to predict survival. LUAD can be classified by the genes linked with cell necrosis and apoptosis. The difference among various types is helpful to deepen the understanding of LUAD. In addition, a risk model was constructed based. In conclusion, this study provides potential detection targets and treatment methods for LUAD from a new perspective.