【Objective】 To investigate the expressions of H3.3G34W, p63 and SATB2 in giant cell tumor of bone (GCTB) and the effect and value of their combined application in the diagnosis of GCTB. 【Methods】 We collected the samples and medical records of 54 cases of GCTB and 83 cases of non-giant cell tumor of bone (14 cases of aneurysmal bone cyst, 16 cases of chondroblastoma and 53 cases of non-ossifying fibroma) diagnosed between 2020 and 2022 in the Department of Pathology of Honghui Hospital Affiliated to Xi’an Jiaotong University. The expressions of H3.3G34W, p63 and SATB2 were detected by EliVision immunohistochemical method. χ² test was used to determine whether there are significant differences in the positive rates of H3.3G34W, p63 and SATB2 among all the groups. The combined diagnostic model including H3.3G34W, p63 and SATB2 was established by Logistic regression analysis, and the diagnostic value of the model was evaluated by ROC curve analysis. 【Results】 The positive rates of H3.3G34W, p63 and SATB2 in GCTB group were 81.5%, 90.7% and 92.6%, respectively; the positive rates in NGCTB group were 2.4%, 28.9% and 62.7%. Compared with NGCTB group, the age of GCTB group was significantly older [(41.222±14.849) vs. (16.566±9.439) , P<0.001] , and the prevalence was higher in women than in men (51.9% vs. 48.1%, P<0.001). In addition, compared with the NGCTB group, the positive rates of H3.3G34W (81.5% vs. 2.4%, P<0.001), p63 (90.7% vs. 28.9%, P<0.001) and SATB2 (92.6% vs. 62.7%, P<0.001) were significantly higher in the GCTB group. Univariate regression analysis built a univariate prediction model and ROC curve analysis showed that age (AUC=92.9%, P<0.001), sex (AUC=64.5%, P=0.004), H3.3G34W positive rate (AUC=89.5%, P<0.001), p63 positive rate (AUC=80.9%, P<0.001) and SATB2 positive rate (AUC=65.0%, P=0.003) were independent predictors of diagnosis of giant cell tumor of bone. Multivariate regression analysis (Logistic) constructed a hybrid prediction model. ROC curve analysis suggested that the hybrid model showed better prediction value than the single factor model (AUC=98.4%, P<0.001). 【Conclusion】 H3.3G34W, p63 and SATB2 are effective molecular markers for the diagnosis of GCTB, and their combined application can improve the prediction efficiency of the diagnosis of GCTB.