Abstract: Accurate and rational classification of rock mass blastability is a critical prerequisite for optimizing blasting parameters and reducing mining costs. To address the fuzziness and randomness inherent in blastability evaluation indices, as well as the issues of subjective bias or objective data deviation found in single-weighting methods, this study establishes a comprehensive evaluation index system. uniaxial compressive strength, density, rock mass integrity coefficient, and specific explosive consumption are selected as key evaluation indicators, with corresponding classification standards defined. Furthermore, a distance function is introduced to calculate the weight preference coefficients for an improved analytic hierarchy process and the entropy weight method, thereby deriving more rational and accurate integrated weights. Finally, the multidimensional connection cloud model was improved by incorporating set pair analysis theory. Combined with the principle of maximum degree of membership, a novel rock mass blastability evaluation method based on integrated weighting and the multidimensional connection cloud model was proposed. The reliability of the model was validated using 29 sets of engineering case samples. Concurrently, Kendall correlation analysis was employed to assess the degree of correlation between each index and rock mass blastability. The results demonstrate that the proposed model achieves an evaluation accuracy of approximately 90% across the 29 samples, showing high consistency with BP Neural Network results. Among the four indicators, uniaxial compressive strength exhibits the strongest correlation with blastability, followed by the rock mass integrity coefficient and density, while specific explosive consumption shows the weakest correlation. Additionally, the correlation coefficients between different indicators range primarily from 0.03 to 0.26, indicating weak inter-correlation and confirming that the selected indices independently reflect rock mass characteristics from distinct dimensions.