<p>The complementarity of ensemble learning primarily stems from the diversity among its classifier members, which is typically assessed by calculating the similarity or distance between classifiers. However, traditional methods for similarity or distance calculation are limited by the representation of classifiers, which may not accurately reflect the true relationships between them, leading to inferior ensemble pruning performance. To address this issue, this study proposes a new classifier representation paradigm and ensemble pruning algorithm(Vec2Q-CEEP). To begin with, we transform the representation of classifiers through spatial mapping, overcoming the limitations of traditional similarity calculation methods, enriching the ways of calculating classifier diversity, and enhancing the distinguishability between classifiers. Furthermore, we introduce a novel similarity measurement method that integrates classifier information from both the original and mapped spaces, enabling a more comprehensive evaluation of the relationships between classifiers. After conducting extensive experiments on 12 UCI datasets, the experimental results demonstrate that this algorithm outperforms several state-of-the-art ensemble pruning algorithms, thus validating its effectiveness and feasibility.</p>