| Since database digital fingerprints with higher performance need to be selected for specific usage scenarios, and the theoretical system of database digital fingerprints need to be improved, it is of great practical significance to conduct comprehensive evaluation on database digital fingerprints. First, aiming at the usage scenarios, we use the performance evaluation indexes of traditional digital fingerprints to establish a hierarchical evaluation index system for database digital fingerprints, and propose a solution for index quantification and pre-processing of evaluation data. The index system can clearly and accurately reflect the logical relationships between the indicators, and comprehensively and objectively describe the performance of database digital fingerprints. Second, a comprehensive evaluation algorithm based on the integrated classification of database digital fingerprints is proposed to directly obtain evaluation results by the traditional evaluation algorithms, and a grading algorithm based on contour coefficients and K-mean clustering is proposed to solve the problem of inconsistent evaluation results between different evaluation algorithms. Third, the ensemble classifier is constructed by using the grading evaluation results and the classification algorithm to realize the accurate grading and comprehensive evaluation on database digital fingerprints. The experimental results show that the evaluation algorithm model based on ensemble classification has better accuracy and generalization than similar classifiers, and can effectively guide decision makers to select digital fingerprints of databases with better performance.