Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability.