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This study focuses on frequency-based textual category feature extraction methods, investigating the application differences and impacts of "representativeness calculation" and "distinctiveness calculation" in Chinese and English text classification. The core objective is to analyze how text feature extraction algorithms interact with language characteristics and to compare the performance of various algorithms across Chinese and English contexts. To achieve this, I propose a novel feature extraction approach and design a series of experiments to evaluate its effectiveness in both Chinese and English corpora. The dataset for this research includes three Chinese datasets (THUCNews, Sougou Chinese Corpus, CNTC) and three English datasets (20 Newsgroups, R8, R52), which encompass diverse textual features and varying data scales. This selection enables a realistic reflection of the performance differences across different linguistic environments and application scenarios, providing comprehensive and reliable data support for evaluating the effectiveness of the experiments.