The evaluation indicator system is the basis of comprehensive evaluation. Whether the selection of evaluation indicators is scientific or not is related to whether the construction of evaluation indicator system is reasonable or not, and it is meaningless to choose the best evaluation method if selection of evaluation indicators is unreasonable. Therefore, how to select evaluation indicators scientifically has always been concerned by researchers. 

Factor analysis and correlation analysis are two kinds of evaluation indicators screening methods which are widely used at present. The purpose of factor analysis is to eliminate the indicators with weak impact on the evaluation results, while the purpose of correlation analysis is to reduce the level of information overlap between the evaluation indicators, so as to reduce distortion of information overlap on comprehensive evaluation results. However, the existing factor analysis indicators screening method is only based on a certain factor load to represent the level of information in the original indicator set interpreted by the indicator, which omits the important role of other factor loads of an indicator in the interpretation of original indicators set information, and cannot comprehensively and accurately represent the level of an indicator to interpret original indicator set information. At the same time, although existing correlation analysis method can reduce the information overlap of indicator set to a certain extent, it is unable to judge whether the overall information overlap between the remaining evaluation indicators is low, and whether it is necessary to further select indicators, so it is very easy to cause the excessive or inadequate of indicators selection. 

In views of the shortcomings of the existing factors analysis and correlation analysis methods, a new method is proposed to screen and weight the evaluation indicator based on information contribution ratio. The information contribution ratio is constructed by factor variance contribution rate and factor loading. The ill-conditioned indicator is introduced in the existing correlation analysis to measure the information overlap level of the indicator set. On this basis, systematic screening and weighting of the evaluation indicators are realized. Finally, this paper takes 500 small business credit business data of a commercial bank in China as an example, and compares the effect of indicators selection with the existing factor analysis and correlation analysis from two aspects of information interpretation ability and overall information overlapping reduction level. 

This paper suggests that the greater the information contribution ratio of the evaluation indicator, the larger the proportion of the explained information of the original indicator set; so the more the indicator should be retained, the greater weight of the indicator should be. It is found that compared with the existing factor analysis method, the information interpretation ability of the retained evaluation indicators by this method proposed in this paper is stronger. At the same time, compared with the existing correlation analysis method, this method can effectively reduce the overall information overlap level of the indicator set. In addition, both of the problems are found in the research and solved by the proposed method that the factor analysis method is easy to delete the indicators with strong ability to information explanation and the correlation analysis method is insufficient in indicators screening.