The occupational risks in coal mining operations are widely acknowledged as one of the most hazardous occupations. Yet, the extant Fine-Kinney (F-K) frameworks are rarely employed to assess these risks within complex uncertainties. Thus, this study develops a T-spherical fuzzy (T-SF)-based F-K decision framework to assess the occupational risks in coal mine enterprises. First, we establish a T-SF linguistic scale to acquire the experts’ subjective risk perception information. It has more flexibility with a dynamic parameter \(\rho \) to capture complex risk information than previous fuzzy sets. Then, the T-SF-weighted Heronian mean aggregation operator with criteria importance through inter-criteria correlation method is presented to collect individual risk rating data. It portrays the dual interrelationships between input data and experts. Next, the stepwise weight assessment ratio analysis approach and the entropy measure are extended to T-SF circumstances to compute the significance of the risk parameters. It computes the integrated significance of risk parameters combined with subjective and objective methods, avoiding inaccuracies with a single method. In conclusion, the F-K framework is augmented by incorporating an integrated CRADIS model based on cumulative prospect theory. This enhancement facilitates modeling the effects of boundedly rational behavior on risk ranking. A numerical example of a coal mining enterprise is provided to showcase the effectiveness and implementation of the proposed F-K framework. The result indicates that \(Ocr_{8}\) “The height of the descending frame is insufficient when replacing the column safety valve” (1.000) has the highest risk priority. Sensitivity and comparative analyses are illustrated to simultaneously evaluate the advantages and robustness of the current F-K framework. Findings indicate that the present F-K framework may provide an efficient tool to tackle occupational risk analysis in coal mine enterprises.
