Osteosarcoma is a cancerous bone tumor that develops from mesenchymal cells and is characterized by early metastasis, easy drug resistance, high disability, and mortality. Immunological characteristics of the tumor microenvironment (TME) have attracted attention for the prognosis and treatment of osteosarcoma, and there is a need to explore a signature with high sensitivity for prognosis. In the present study, a total of 84 samples of osteosarcoma were acquired from the UCSC Xena database, analyzed for immune infiltration and classified into two categories depending on their immune properties, and then screened for DEGs between the two groups and analyzed for enrichment, with the majority of DEGs enriched in the immune domain. To further analyze their immune characteristics, the immune-related genes were obtained from the TIMER database. We performed an intersection analysis to identify immune-related differentially expressed genes (IR-DEGs), which were analyzed using a univariate COX regression, and LASSO analysis was used to obtain the ideal genes to construct the risk model, and to uncover the prognostic distinctions between high-risk scoring group and low-risk scoring group, a survival analysis was conducted. The risk assessment model developed in this study revealed a notable variation in survival analysis outcomes between the high-risk and low-risk scoring groups, and the conclusions reached by the model are consistent with the findings of previous scholars. They also yield meaningful results when analyzing immune checkpoints. The risk assessment model developed in this study is precise and dependable for forecasting outcomes and analyzing characteristics of osteosarcoma.