This study validated the bi-factor structural construct of the Nursing Burnout Scale (NBS) by using a stratified sample of 523 nurses from Jiang Shu, China, who completed the short version of the NBS survey in Chinese. Mplus software (version 8.1) provided the statistical outputs for all analyses conducted in this study. The findings for four alternative burnout constructs-single-, three-, high-order, and bi-factor models-employing confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) approaches improved our understanding of work burnout among nurses in the hospitals in China. Statistical comparison among a series of nested CFA and ESEM models revealed that a bi-factor model with a global burnout factor and three content-specific factors-emotional exhaustion (EE), depersonalization (DP), and reduced personal accomplishment (PA)-involving the ESEM approach outperformed all other models in terms of the model data fit. Only global burnout factor and the content-specific DP factor explained the considerable unique proportion of variance in the observed scores. The content-specific factors EE and PA only explained a small unique proportion of variance in the observed scores. The findings support the bi-factor structure of the NBS that was originally proposed by Mészáros et al. in Hungary (2014). Moreover, the findings provide insights into understanding the cross-culture and cross-situation factorial consistency of nursing burnout. Compared with CFA, ESEM is a better approach for validating the bi-factor factorial structure. The findings improve our understanding of the complexity of the multidimensional structure of job burnout among nurses. Moreover, the proposed method provides hospital administrators with the total burnout score to ensure timely intervention for the staff who are at risk. However, the EE and PA scores should be interpreted carefully because these scores reflect a very small proportion of the burnout content. Further investigation on invariance measurement based on gender and time will provide insights into the generalizability of the bi-factor nursing burnout structure.