Abstract: In order to accurately classify the seismic failure modes of reinforced concrete (RC) columns, a two-stage logistic regression model was proposed based on an exhaustive search strategy and on a logistic regression algorithm. The optimal characteristic parameters to classify flexure failure and non-flexure failure as well as flexure-shear failure and shear failure were selected respectively based on the exhaustive search strategy. A two-stage logistic regression model to classify the seismic failure modes of RC columns was established by combining the optimal characteristic parameters with the logistic regression algorithm. The classification accuracy of the proposed model was validated by comparing the new method with traditional methods. The analysis results show that the model not only constructs the explicit function relationship between the characteristic parameters and the seismic failure modes, but also overcomes the defect of poor interpretation of the prediction results in the traditional 'black box' machine learning discriminant methods. Moreover, through the reasonable selection of the optimal characteristic parameters, the function form of the discriminant model is reasonably simplified on the premise of ensuring the discrimination accuracy. It solves the problems of high complexity and low computational efficiency of the discrimination model in the traditional machine learning discrimination methods. For the three seismic failure modes of RC column, the overall discrimination accuracy of this model is more than 90%, which is about 5% higher than that of the classical logistic regression algorithm and 20% higher than that of the traditional empirical discrimination method.