[Objective] Based on the comprehensive perspective of the diversity of identification index information and the combination of different weighting and sorting algorithms, combined with the characteristics of large-scale data sets, the construction of core patent portfolio identification methods and their application comparisons are studied.[Methods] Through cross-combination, 5 combined identification methods are constructed, and 6 patent feature information is selected. Taking the field of artificial intelligence as an example, the characteristics and application scenarios of each method are compared from the overall and local levels. [Results] Different combined identification methods maintain high consistency when applied to different datasets and time periods. At the same time, as the number of core patents to be identified increases, the coincidence rate between the two methods gradually decreases. For example, the core patent coincidence rate of method 1 and method 4 has dropped from 80% to 47%. [Limitations] Only one field is applied, and the application characteristics of combination method can be further excavated. [Conclusions] The five combined identification methods constructed can be applied to different results requirements and specific situations of core patent identification based on the scale, dispersion, time span and feature value performance of patent data sets and differences in the development of technical fields. For the rapidly developing field of artificial intelligence, the two methods of entropy weight method weighting combined with grey relational analysis and entropy weight method weighting combined with TOPSIS have better recognition effect.