<p>Conducting multi-scale visual analyses of carrier-based aircraft deck support operation scenarios is of significant strategic importance for enhancing aircraft carrier combat effectiveness. However, existing methods primarily focus on task recognition and lack intuitive, interactive analysis of the spatiotemporal coupling and evolutionary processes among multiple tasks. Moreover, these recognition approaches rely on high-quality annotated datasets, limiting their applicability in real-world scenarios. To address these challenges, we propose a visualization-based analytical method tailored for deck support operation scenarios. Specifically, to accurately and efficiently recognize tasks, we introduce a feature-enhanced clustering-based task recognition method that improves trajectory representations by incorporating both spatiotemporal and discriminative features. Building on this foundation, we develop <i>DeckVis</i>, a visualization system that presents the spatiotemporal features of tasks through coordinated multi-view displays, thereby enabling interactive, multi-scale scenario analysis. A comparative analysis, carefully designed case studies, and in-depth user evaluations demonstrate the effectiveness of our recognition method and visual system.</p>