The various failure modes of reinforced concrete (RC) beam-column joints under lateral loading exert different impacts on the structural performance. Thus, accurately categorizing component failure modes is pivotal for determining the deformation performance limits in structural performance design. Existing research lacks clear boundaries between different failure modes of beam-column joints, and it remains difficult to distinguish intervals corresponding to distinct failure types using a single parameter. This paper proposes a more accurate and practical discriminant method for identifying failure modes in interior beam-column joints, incorporating multiple parameters based on the Fisher transform and Bayes classification principles. The method initially employs the Fisher discriminant analysis to identify projection spaces with maximum separation between classes, projecting original samples into these optimally separated spaces to obtain new samples more amenable to classification. Subsequently, Bayes classification principles are applied for discriminant analysis of the new samples. In studying interior joint failure mode classification, based on this method the corresponding multi-parameter classification discriminant equations are established using a combination of four parameters: axial compression ratio, shear compression ratio, concrete strength, and stirrup characteristic value. This approach effectively classifies failure modes of interior beam-column joints and clearly defines intervals corresponding to different failure types. Furthermore, through sensitivity analysis of influencing factors, the study determines that the shear compression ratio has the most significant influence on failure modes, followed by the stirrup characteristic value. Therefore, adjusting the shear compression ratio and the stirrup characteristic value is an effective means of preventing shear failure at this type of joint.